Deep Learning Applications

Applications

DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations

• arxiv: http://arxiv.org/abs/1510.02927

Some like it hot - visual guidance for preference prediction

• arxiv: http://arxiv.org/abs/1510.07867
• demo: http://howhot.io/

Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey

• arxiv: http://arxiv.org/abs/1512.03131

Deep Relative Attributes

• intro: ACCV 2016
• arxiv: http://arxiv.org/abs/1512.04103
• github: https://github.com/yassersouri/ghiaseddin

Deep-Spying: Spying using Smartwatch and Deep Learning

• arxiv: http://arxiv.org/abs/1512.05616
• github: https://github.com/tonybeltramelli/Deep-Spying

Camera identification with deep convolutional networks

• key word: copyright infringement cases, ownership attribution
• arxiv: http://arxiv.org/abs/1603.01068

An Analysis of Deep Neural Network Models for Practical Applications

• arxiv: http://arxiv.org/abs/1605.07678

8 Inspirational Applications of Deep Learning

• intro: Colorization of Black and White Images, Adding Sounds To Silent Movies, Automatic Machine Translation Object Classification in Photographs, Automatic Handwriting Generation, Character Text Generation, Image Caption Generation, Automatic Game Playing
• blog: http://machinelearningmastery.com/inspirational-applications-deep-learning/

16 Open Source Deep Learning Models Running as Microservices

• intro: Places 365 Classifier, Deep Face Recognition, Real Estate Classifier, Colorful Image Colorization, Illustration Tagger, InceptionNet, Parsey McParseface, ArtsyNetworks
• blog: http://blog.algorithmia.com/2016/07/open-source-deep-learning-algorithm-roundup/

Deep Cascaded Bi-Network for Face Hallucination

• project page: http://mmlab.ie.cuhk.edu.hk/projects/CBN.html
• arxiv: http://arxiv.org/abs/1607.05046

DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation

• project page: http://yaroslav.ganin.net/static/deepwarp/
• arxiv: http://arxiv.org/abs/1607.07215

Autoencoding Blade Runner

• blog: https://medium.com/@Terrybroad/autoencoding-blade-runner-88941213abbe#.9kckqg7cq
• github: https://github.com/terrybroad/Learned-Sim-Autoencoder-For-Video-Frames

A guy trained a machine to “watch” Blade Runner. Then things got seriously sci-fi.

http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encoding

Deep Convolution Networks for Compression Artifacts Reduction

• intro: ICCV 2015
• project page(code): http://mmlab.ie.cuhk.edu.hk/projects/ARCNN.html
• arxiv: http://arxiv.org/abs/1608.02778

Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

• intro: Deep Genomic Dashboard (Deep GDashboard)
• arxiv: http://arxiv.org/abs/1608.03644

Instagram photos reveal predictive markers of depression

• arxiv: http://arxiv.org/abs/1608.03282

How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos

• review: https://www.technologyreview.com/s/602208/how-an-algorithm-learned-to-identify-depressed-individuals-by-studying-their-instagram/

IM2CAD

• arxiv: http://arxiv.org/abs/1608.05137

Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks

• paper: https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher
• github: https://github.com/cupslab/neural_network_cracking

Defeating Image Obfuscation with Deep Learning

• arxiv: http://arxiv.org/abs/1609.00408

Detecting Music BPM using Neural Networks

• keywords: BPM (Beats Per Minutes)
• blog: https://nlml.github.io/neural-networks/detecting-bpm-neural-networks/
• github: https://github.com/nlml/bpm

Generative Visual Manipulation on the Natural Image Manifold

• intro: ECCV 2016
• project page: https://people.eecs.berkeley.edu/~junyanz/projects/gvm/
• arxiv: http://arxiv.org/abs/1609.03552
• github: https://github.com/junyanz/iGAN

Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

• arxiv: http://arxiv.org/abs/1609.05119

Deep Gold: Using Convolution Networks to Find Minerals

• blog: https://hackernoon.com/deep-gold-using-convolution-networks-to-find-minerals-aafdb37355df#.lgh95ub4a
• github: https://github.com/scottvallance/DeepGold

Predicting First Impressions with Deep Learning

• arxiv: https://arxiv.org/abs/1610.08119

Judging a Book By its Cover

• arxiv: https://arxiv.org/abs/1610.09204
• review: https://www.technologyreview.com/s/602807/deep-neural-network-learns-to-judge-books-by-their-covers/

Image Credibility Analysis with Effective Domain Transferred Deep Networks

• arxiv: https://arxiv.org/abs/1611.05328

A novel image tag completion method based on convolutional neural network

• arxiv: https://www.arxiv.org/abs/1703.00586

Image operator learning coupled with CNN classification and its application to staff line removal

• intro: ICDAR 2017
• arxiv: https://arxiv.org/abs/1709.06476

Joint Image Filtering with Deep Convolutional Networks

• intro: University of California, Merced & Virginia Tech & University of Illinois
• project page: http://vllab1.ucmerced.edu/~yli62/DJF_residual/
• arxiv: https://arxiv.org/abs/1710.04200
• github: https://github.com/Yijunmaverick/DeepJointFilter

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1704.02470
• github: https://github.com/aiff22/DPED

Neural Scene De-rendering

• intro: CVPR 2017
• project page: http://nsd.csail.mit.edu/
• paper: http://nsd.csail.mit.edu/papers/nsd_cvpr.pdf
• gihtub: https://github.com/jiajunwu/nsd

Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks

• intro: WACV 2018
• project page: http://bvision11.cs.unc.edu/bigpen/yipin/WACV2018/
• arxiv: https://arxiv.org/abs/1801.09042

Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata

https://arxiv.org/abs/1801.09108

Single Image Reflection Removal Using Deep Encoder-Decoder Network

https://arxiv.org/abs/1802.00094

Learning Deep Convolutional Networks for Demosaicing

https://arxiv.org/abs/1802.03769

Fully convolutional watermark removal attack

• github: https://github.com/marcbelmont/cnn-watermark-removal

ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes

• arxiv: https://arxiv.org/abs/1803.10562
• github: https://github.com/Prinsphield/ELEGANT

Learning to See in the Dark

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1805.01934
• video: https://www.youtube.com/watch?v=qWKUFK7MWvg&feature=youtu.be

Boundary / Edge / Contour Detection

Holistically-Nested Edge Detection

• intro: ICCV 2015, Marr Prize
• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xie_Holistically-Nested_Edge_Detection_ICCV_2015_paper.pdf
• arxiv: http://arxiv.org/abs/1504.06375
• github: https://github.com/s9xie/hed

Unsupervised Learning of Edges

• intro: CVPR 2016. Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.04166
• zn-blog: http://www.leiphone.com/news/201607/b1trsg9j6GSMnjOP.html

Pushing the Boundaries of Boundary Detection using Deep Learning

• arxiv: http://arxiv.org/abs/1511.07386

Convolutional Oriented Boundaries

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.02755

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

• project page: http://www.vision.ee.ethz.ch/~cvlsegmentation/
• arxiv: https://arxiv.org/abs/1701.04658
• github: https://github.com/kmaninis/COB

Richer Convolutional Features for Edge Detection

• intro: CVPR 2017
• keywords: richer convolutional features (RCF)
• arxiv: https://arxiv.org/abs/1612.02103
• github: https://github.com/yun-liu/rcf

Contour Detection from Deep Patch-level Boundary Prediction

https://arxiv.org/abs/1705.03159

CASENet: Deep Category-Aware Semantic Edge Detection

• intro: CVPR 2017. CMU & Mitsubishi Electric Research Laboratories (MERL)
• arxiv: https://arxiv.org/abs/1705.09759
• code: http://www.merl.com/research/license#CASENet
• video: https://www.youtube.com/watch?v=BNE1hAP6Qho

Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1801.00524

Deep Crisp Boundaries: From Boundaries to Higher-level Tasks

https://arxiv.org/abs/1801.02439

Image Processing

Fast Image Processing with Fully-Convolutional Networks

• intro: ICCV 2017. Qifeng Chen (陈启峰)
• project page: http://www.cqf.io/ImageProcessing/
• arxiv: https://arxiv.org/abs/1709.00643
• supp: https://youtu.be/eQyfHgLx8Dc
• github: https://github.com/CQFIO/FastImageProcessing

DeepISP: Learning End-to-End Image Processing Pipeline

https://arxiv.org/abs/1801.06724

Image-Text

Learning Two-Branch Neural Networks for Image-Text Matching Tasks

https://arxiv.org/abs/1704.03470

Dual-Path Convolutional Image-Text Embedding

• arxiv: https://arxiv.org/abs/1711.05535
• github: https://github.com//layumi/Image-Text-Embedding

Conditional Image-Text Embedding Networks

https://arxiv.org/abs/1711.08389

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

https://arxiv.org/abs/1711.10485

Stacked Cross Attention for Image-Text Matching

https://arxiv.org/abs/1803.08024

Age Estimation

Deeply-Learned Feature for Age Estimation

• paper: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7045931&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7045931

Age and Gender Classification using Convolutional Neural Networks

• paper: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/CNN_AgeGenderEstimation.pdf
• project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
• github: https://github.com/GilLevi/AgeGenderDeepLearning

Group-Aware Deep Feature Learning For Facial Age Estimation

• paper: http://www.sciencedirect.com/science/article/pii/S0031320316303417

Local Deep Neural Networks for Age and Gender Classification

https://arxiv.org/abs/1703.08497

Understanding and Comparing Deep Neural Networks for Age and Gender Classification

https://arxiv.org/abs/1708.07689

Age Group and Gender Estimation in the Wild with Deep RoR Architecture

• intro: IEEE ACCESS
• arxiv: https://arxiv.org/abs/1710.02985

Age and gender estimation based on Convolutional Neural Network and TensorFlow

https://github.com/BoyuanJiang/Age-Gender-Estimate-TF

Deep Regression Forests for Age Estimation

• intro: Shanghai University & Johns Hopkins University & Nankai University
• arxiv: https://arxiv.org/abs/1712.07195

Face Aging

Recurrent Face Aging

• paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf

Face Aging With Conditional Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1702.01983

Learning Face Age Progression: A Pyramid Architecture of GANs

https://arxiv.org/abs/1711.10352

Face Aging with Contextual Generative Adversarial Nets

• intro: ACM Multimedia 2017
• arxiv: https://arxiv.org/abs/1802.00237

Recursive Chaining of Reversible Image-to-image Translators For Face Aging

https://arxiv.org/abs/1802.05023

Emotion Recognition / Expression Recognition

Real-time emotion recognition for gaming using deep convolutional network features

• paper: http://arxiv.org/abs/1408.3750v1
• code: https://github.com/Zebreu/ConvolutionalEmotion

Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

• project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
• paper: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/LeviHassnerICMI15.pdf
• github: https://gist.github.com/GilLevi/54aee1b8b0397721aa4b
• blog: https://gilscvblog.com/2017/01/31/emotion-recognition-in-the-wild-via-convolutional-neural-networks-and-mapped-binary-patterns/

DeXpression: Deep Convolutional Neural Network for Expression Recognition

• paper: http://arxiv.org/abs/1509.05371

DEX: Deep EXpectation of apparent age from a single image

• intro: ICCV 2015
• paper: https://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf
• homepage: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/

EmotioNet: EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild

• intro: CVPR 2016
• paper: http://cbcsl.ece.ohio-state.edu/cvpr16.pdf
• database: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html

How Deep Neural Networks Can Improve Emotion Recognition on Video Data

• intro: ICIP 2016
• arxiv: http://arxiv.org/abs/1602.07377

Peak-Piloted Deep Network for Facial Expression Recognition

• arxiv: http://arxiv.org/abs/1607.06997

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

• arxiv: http://arxiv.org/abs/1608.01041

A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset

• arxiv: http://arxiv.org/abs/1608.01647

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

• arxiv: http://arxiv.org/abs/1609.06591

EmotionNet Challenge

• homrepage: http://cbcsl.ece.ohio-state.edu/EmotionNetChallenge/index.html
• dataset: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html

Baseline CNN structure analysis for facial expression recognition

• intro: RO-MAN2016 Conference
• arxiv: https://arxiv.org/abs/1611.04251

Facial Expression Recognition using Convolutional Neural Networks: State of the Art

• arxiv: https://arxiv.org/abs/1612.02903

DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network

• arxiv: https://arxiv.org/abs/1702.04280
• api: https://www.sighthound.com/products/cloud

Deep generative-contrastive networks for facial expression recognition

https://arxiv.org/abs/1703.07140

Convolutional Neural Networks for Facial Expression Recognition

https://arxiv.org/abs/1704.06756

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1704.08619

Spatial-Temporal Recurrent Neural Network for Emotion Recognition

https://arxiv.org/abs/1705.04515

Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units

https://arxiv.org/abs/1706.01509

Temporal Multimodal Fusion for Video Emotion Classification in the Wild

https://arxiv.org/abs/1709.07200

Island Loss for Learning Discriminative Features in Facial Expression Recognition

https://arxiv.org/abs/1710.03144

Real-time Convolutional Neural Networks for Emotion and Gender Classification

https://arxiv.org/abs/1710.07557

Attribution Prediction

PANDA: Pose Aligned Networks for Deep Attribute Modeling

• intro: Facebook. CVPR 2014
• arxiv: http://arxiv.org/abs/1311.5591
• github: https://github.com/facebook/pose-aligned-deep-networks

Predicting psychological attributions from face photographs with a deep neural network

• arxiv: http://arxiv.org/abs/1512.01289

Learning Human Identity from Motion Patterns

• arxiv: http://arxiv.org/abs/1511.03908

Sentiment Analysis / Sentiment Prediction

From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

• arxiv: http://arxiv.org/abs/1604.03489
• github: https://github.com/imatge-upc/sentiment-2016
• gitxiv: http://gitxiv.com/posts/ruqRgXdPTHJ77LDEb/from-pixels-to-sentiment-fine-tuning-cnns-for-visual

Predict Sentiment From Movie Reviews Using Deep Learning

• blog: http://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/

Neural Sentiment Classification with User and Product Attention

• intro: EMNLP 2016
• paper: http://www.thunlp.org/~chm/publications/emnlp2016_NSCUPA.pdf
• github: https://github.com/thunlp/NSC

From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

• intro: Accepted for publication in Image and Vision Computing
• arxiv: https://arxiv.org/abs/1604.03489
• github: https://github.com/imatge-upc/sentiment-2016

Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks

• intro: ICIP 2017
• arxiv: https://arxiv.org/abs/1711.07798

Deep Learning for Sentiment Analysis : A Survey

• intro: LinkedIn & University of Illinois at Chicago
• arxiv: https://arxiv.org/abs/1801.07883

Place Recognition

NetVLAD: CNN architecture for weakly supervised place recognition

• intro: CVPR 2016
• intro: Google Street View Time Machine, soft-assignment, Weakly supervised triplet ranking loss
• homepage: http://www.di.ens.fr/willow/research/netvlad/
• arxiv: http://arxiv.org/abs/1511.07247

PlaNet - Photo Geolocation with Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1602.05314
• review(“Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image”): https://www.technologyreview.com/s/600889/google-unveils-neural-network-with-superhuman-ability-to-determine-the-location-of-almost/
• github(“City-Recognition: CS231n Project for Winter 2016”): https://github.com/dmakian/LittlePlaNet
• github: https://github.com/wulfebw/LittlePlaNet-Models

Visual place recognition using landmark distribution descriptors

• arxiv: http://arxiv.org/abs/1608.04274

Low-effort place recognition with WiFi fingerprints using deep learning

• arxiv: https://arxiv.org/abs/1611.02049
• github: https://github.com/aqibsaeed/Place-Recognition-using-Autoencoders-and-NN
• github(Keras): https://github.com/mallsk23/place_recognition_wifi_fingerprints_deep_learning

Deep Learning Features at Scale for Visual Place Recognition

• intro: ICRA 2017
• arxiv: https://arxiv.org/abs/1701.05105

Place recognition: An Overview of Vision Perspective

https://arxiv.org/abs/1707.03470

Camera Relocalization

PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

• paper: http://arxiv.org/abs/1505.07427
• project page: http://mi.eng.cam.ac.uk/projects/relocalisation/#results
• github: https://github.com/alexgkendall/caffe-posenet
• github(TensorFlow): https://github.com/kentsommer/tensorflow-posenet

Modelling Uncertainty in Deep Learning for Camera Relocalization

• paper: http://arxiv.org/abs/1509.05909

Random Forests versus Neural Networks - What’s Best for Camera Relocalization?

• arxiv: http://arxiv.org/abs/1609.05797

Deep Convolutional Neural Network for 6-DOF Image Localization

• arxiv: https://arxiv.org/abs/1611.02776

Image-based Localization with Spatial LSTMs

• arxiv: https://arxiv.org/abs/1611.07890

VidLoc: 6-DoF Video-Clip Relocalization

• arxiv: https://arxiv.org/abs/1702.06521

Towards CNN Map Compression for camera relocalisation

• arxiv: https://www.arxiv.org/abs/1703.00845

Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network

• intro: Aalto University & Indian Institute of Technology
• arxiv: https://arxiv.org/abs/1707.09733

MapNet: Geometry-Aware Learning of Maps for Camera Localization

• intro: Georgia Institute of Technology & NVIDIA
• arxiv: https://arxiv.org/abs/1712.03342

Counting Objects

Towards perspective-free object counting with deep learning

• intro: ECCV 2016. Counting CNN and Hydra CNN
• paper: http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf
• github: https://github.com/gramuah/ccnn
• poster: http://www.eccv2016.org/files/posters/P-3B-26.pdf

Using Convolutional Neural Networks to Count Palm Trees in Satellite Images

• arxiv: https://arxiv.org/abs/1701.06462

Count-ception: Counting by Fully Convolutional Redundant Counting

https://arxiv.org/abs/1703.08710

Counting Objects with Faster R-CNN

• blog: https://softwaremill.com/counting-objects-with-faster-rcnn/
• github: https://github.com/softberries/keras-frcnn

Drone-based Object Counting by Spatially Regularized Regional Proposal Network

https://arxiv.org/abs/1707.05972

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

• intro: ICCV 2017. CMU & Universidade de Lisboa
• arxiv: https://arxiv.org/abs/1707.09476

Representation Learning by Learning to Count

• intro: ICCV 2017 oral
• arxiv: https://arxiv.org/abs/1708.06734

Leaf Counting with Deep Convolutional and Deconvolutional Networks

• intro: ICCV 2017 Workshop on Computer Vision Problems in Plant Phenotyping
• arxiv: https://arxiv.org/abs/1708.07570

Improving Object Counting with Heatmap Regulation

https://arxiv.org/abs/1803.05494

Learning Short-Cut Connections for Object Counting

• keywords: Gated U-Net (GU-Net)
• arxiv: https://arxiv.org/abs/1805.02919

Crowd Counting / Crowd Analysis

Large scale crowd analysis based on convolutional neural network

• paper: http://www.sciencedirect.com/science/article/pii/S0031320315001259

Deep People Counting in Extremely Dense Crowds

• intro: ACM 2015
• paper: http://yangliang.github.io/pdf/sp055u.pdf

Crossing-line Crowd Counting with Two-phase Deep Neural Networks

• intro: ECCV 2016
• paper: http://www.ee.cuhk.edu.hk/~rzhao/project/crossline_eccv16/ZhaoLZWeccv16.pdf
• poster: http://www.eccv2016.org/files/posters/P-3C-41.pdf

Cross-scene Crowd Counting via Deep Convolutional Neural Networks

• intro: CVPR 2015
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/zhangLWYcvpr15.pdf

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

• intro: CVPR 2016
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf
• paper: http://sist.shanghaitech.edu.cn/office/research/news/CVPR2016/paper/Single-Image%20Crowd%20Counting%20via%20Multi-Column%20Convolutional%20Neural%20Network.pdf
• dataset(pwd: p1rv): http://pan.baidu.com/s/1gfyNBTh
• slides: http://smartdsp.xmu.edu.cn/%E6%B1%87%E6%8A%A5pdf/crowd%20counting%E6%9E%97%E8%B4%A8%E9%94%90.pdf

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

• intro: ACM Multimedia (MM) 2016
• arxiv: http://arxiv.org/abs/1608.06197

Crowd Counting by Adapting Convolutional Neural Networks with Side Information

• arxiv: https://arxiv.org/abs/1611.06748

Fully Convolutional Crowd Counting On Highly Congested Scenes

• intro: VISAPP 2017
• arxiv: https://arxiv.org/abs/1612.00220

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

• intro: AAAI 2017
• project page: https://www.microsoft.com/en-us/research/publication/deep-spatio-temporal-residual-networks-for-citywide-crowd-flows-prediction/
• paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ST-ResNet-AAAI17-Zhang.pdf
• github: https://github.com/lucktroy/DeepST/tree/master/scripts/papers/AAAI17
• ppt: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/DeepST-crowd-prediction.pptx
• system: http://urbanflow.sigkdd.com.cn/

Multi-scale Convolutional Neural Networks for Crowd Counting

• arxiv: https://arxiv.org/abs/1702.02359

Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting

https://arxiv.org/abs/1703.09393

Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking

https://arxiv.org/abs/1705.10118

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification

• intro: AVSS 2017
• arxiv: https://arxiv.org/abs/1705.10698

Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1706.03725

A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

https://arxiv.org/abs/1707.01202

Spatiotemporal Modeling for Crowd Counting in Videos

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1707.07890

CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting

• intro: AVSS 2017 (14th International Conference on Advanced Video and Signal Based Surveillance)
• arxiv: https://arxiv.org/abs/1707.09605

Switching Convolutional Neural Network for Crowd Counting

• intro: CVPR 2017. Indian Institute of Science
• project page: http://val.serc.iisc.ernet.in/crowdcnn/
• arxiv: https://arxiv.org/abs/1708.00199
• github: https://github.com/val-iisc/crowd-counting-scnn

Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.00953

Deep Spatial Regression Model for Image Crowd Counting

https://arxiv.org/abs/1710.09757

Crowd counting via scale-adaptive convolutional neural network

• intro: WACV 2-18. Tencent Youtu Lab
• arxiv: https://arxiv.org/abs/1711.04433
• github: https://github.com/miao0913/SaCNN-CrowdCounting-Tencent_Youtu

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

https://arxiv.org/abs/1712.06679

Structured Inhomogeneous Density Map Learning for Crowd Counting

https://arxiv.org/abs/1801.06642

Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1801.08391

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.03095

Crowd Counting via Adversarial Cross-Scale Consistency Pursuit

• intro: CVPR 2018

An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

• intro: CVPR 2018 Workshop On Visual Understanding of Humans in Crowd Scene
• arxiv: https://arxiv.org/abs/1804.07821

Activity Recognition

Implementing a CNN for Human Activity Recognition in Tensorflow

• blog: http://aqibsaeed.github.io/2016-11-04-human-activity-recognition-cnn/
• github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN

Concurrent Activity Recognition with Multimodal CNN-LSTM Structure

• arxiv: https://arxiv.org/abs/1702.01638

CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.03058

Deploying Tensorflow model on Andorid device for Human Activity Recognition

• blog: http://aqibsaeed.github.io/2017-05-02-deploying-tensorflow-model-andorid-device-human-activity-recognition/
• github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN/tree/master/ActivityRecognition

Music Classification / Sound Classification

Explaining Deep Convolutional Neural Networks on Music Classification

• arxiv: http://arxiv.org/abs/1607.02444
• blog: https://keunwoochoi.wordpress.com/2015/12/09/ismir-2015-lbd-auralisation-of-deep-convolutional-neural-networks-listening-to-learned-features-auralization/
• blog: https://keunwoochoi.wordpress.com/2016/03/23/what-cnns-see-when-cnns-see-spectrograms/
• github: https://github.com/keunwoochoi/Auralisation
• audio samples: https://soundcloud.com/kchoi-research

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

• project page: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/main.html
• arxiv: http://arxiv.org/abs/1608.04363

Convolutional Recurrent Neural Networks for Music Classification

• arxiv: http://arxiv.org/abs/1609.04243
• blog: https://keunwoochoi.wordpress.com/2016/09/15/paper-is-out-convolutional-recurrent-neural-networks-for-music-classification/
• github: https://github.com/keunwoochoi/music-auto_tagging-keras

CNN Architectures for Large-Scale Audio Classification

• intro: Google
• arxiv: https://arxiv.org/abs/1609.09430
• demo: https://www.youtube.com/watch?v=oAAo_r7ZT8U&feature=youtu.be

SoundNet: Learning Sound Representations from Unlabeled Video

• intro: MIT. NIPS 2016
• project page: http://projects.csail.mit.edu/soundnet/
• arxiv: https://arxiv.org/abs/1610.09001
• paper: http://web.mit.edu/vondrick/soundnet.pdf
• github: https://github.com/cvondrick/soundnet
• github: https://github.com/eborboihuc/SoundNet-tensorflow
• youtube: https://www.youtube.com/watch?v=yJCjVvIY4dU

Deep Learning ‘ahem’ detector

• github: https://github.com/worldofpiggy/deeplearning-ahem-detector
• slides: https://docs.google.com/presentation/d/1QXQEOiAMj0uF2_Gafr2bn-kMniUJAIM1PLTFm1mUops/edit#slide=id.g35f391192_00
• mirror: https://pan.baidu.com/s/1c2KGlwO

GenreFromAudio: Finding the genre of a song with Deep Learning

• intro: A pipeline to build a dataset from your own music library and use it to fill the missing genres
• github: https://github.com/despoisj/DeepAudioClassification

TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

• arxiv: https://arxiv.org/abs/1703.10667
• github: https://github.com/chihyaoma/Activity-Recognition-with-CNN-and-RNN

On the Robustness of Deep Convolutional Neural Networks for Music Classification

• intro: Queen Mary University of London & New York University
• arxiv: https://arxiv.org/abs/1706.02361

NSFW Detection / Classification

Nipple Detection using Convolutional Neural Network

• reddit: https://www.reddit.com/over18?dest=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2F33n77s%2Fandroid_app_nipple_detection_using_convolutional%2F

Applying deep learning to classify pornographic images and videos

• arxiv: http://arxiv.org/abs/1511.08899

MODERATE, FILTER, OR CURATE ADULT CONTENT WITH CLARIFAI’S NSFW MODEL

• blog: http://blog.clarifai.com/moderate-filter-or-curate-adult-content-with-clarifais-nsfw-model/#.VzVhM-yECZY

WHAT CONVOLUTIONAL NEURAL NETWORKS LOOK AT WHEN THEY SEE NUDITY

• blog: http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity#.VzVh_-yECZY

Open Sourcing a Deep Learning Solution for Detecting NSFW Images

• intro: Yahoo
• blog: https://yahooeng.tumblr.com/post/151148689421/open-sourcing-a-deep-learning-solution-for
• github: https://github.com/yahoo/open_nsfw

Miles Deep - AI Porn Video Editor

• intro: Deep Learning Porn Video Classifier/Editor with Caffe
• github: https://github.com/ryanjay0/miles-deep

Image Reconstruction / Inpainting

Context Encoders: Feature Learning by Inpainting

• intro: CVPR 2016
• intro: Unsupervised Feature Learning by Image Inpainting using GANs
• project page: http://www.cs.berkeley.edu/~pathak/context_encoder/
• arxiv: https://arxiv.org/abs/1604.07379
• github(official): https://github.com/pathak22/context-encoder
• github: https://github.com/BoyuanJiang/context_encoder_pytorch

Semantic Image Inpainting with Perceptual and Contextual Losses

Semantic Image Inpainting with Deep Generative Models

• keywords: Deep Convolutional Generative Adversarial Network (DCGAN)
• arxiv: http://arxiv.org/abs/1607.07539
• github: https://github.com/bamos/dcgan-completion.tensorflow

High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

• intro: University of Southern California & Adobe Research
• arxiv: https://arxiv.org/abs/1611.09969

Face Image Reconstruction from Deep Templates

https://www.arxiv.org/abs/1703.00832

Deep Learning-Guided Image Reconstruction from Incomplete Data

https://arxiv.org/abs/1709.00584

Image Inpainting using Multi-Scale Feature Image Translation

https://arxiv.org/abs/1711.08590

Image Inpainting for High-Resolution Textures using CNN Texture Synthesis

https://arxiv.org/abs/1712.03111

Context-Aware Semantic Inpainting

https://arxiv.org/abs/1712.07778

Deep Blind Image Inpainting

https://arxiv.org/abs/1712.09078

Deep Stacked Networks with Residual Polishing for Image Inpainting

https://arxiv.org/abs/1801.00289

Light-weight pixel context encoders for image inpainting

https://arxiv.org/abs/1801.05585

Deep Structured Energy-Based Image Inpainting

https://arxiv.org/abs/1801.07939

Shift-Net: Image Inpainting via Deep Feature Rearrangement

https://arxiv.org/abs/1801.09392

Cascade context encoder for improved inpainting

https://arxiv.org/abs/1803.04033

Image Restoration

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

• intro: NIPS 2016
• arxiv: http://arxiv.org/abs/1603.09056

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

• arxiv: http://arxiv.org/abs/1606.08921

Image Completion with Deep Learning in TensorFlow

• blog: http://bamos.github.io/2016/08/09/deep-completion/

Deeply Aggregated Alternating Minimization for Image Restoration

• arxiv: https://arxiv.org/abs/1612.06508

A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction

• intro: Seoul National University
• arxiv: https://arxiv.org/abs/1701.06190

MemNet: A Persistent Memory Network for Image Restoration

• intro: ICCV 2017 (Spotlight presentation)
• arxiv: https://arxiv.org/abs/1708.02209
• github: https://github.com/tyshiwo/MemNet

Deep Mean-Shift Priors for Image Restoration

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1709.03749

xUnit: Learning a Spatial Activation Function for Efficient Image Restoration

• arxiv: https://arxiv.org/abs/1711.06445
• github: https://github.com/kligvasser/xUnit

Deep Image Prior

• intro: Skolkovo Institute of Science and Technology & University of Oxford
• project page: https://dmitryulyanov.github.io/deep_image_prior
• arxiv: https://arxiv.org/abs/1711.10925
• paper: https://sites.skoltech.ru/app/data/uploads/sites/25/2017/11/deep_image_prior.pdf
• github: https://github.com//DmitryUlyanov/deep-image-prior
• reddit: https://www.reddit.com/r/MachineLearning/comments/7gls3j/r_deep_image_prior_deep_superresolution/

MemNet: A Persistent Memory Network for Image Restoration

• intro: ICCV 2017 spotlight
• paper: http://cvlab.cse.msu.edu/pdfs/Image_Restoration%20using_Persistent_Memory_Network.pdf
• github: https://github.com//tyshiwo/MemNet

Denoising Prior Driven Deep Neural Network for Image Restoration

https://arxiv.org/abs/1801.06756

Face Completion

Generative Face Completion

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.05838

High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks

• intro: North Carolina State University
• arxiv: https://arxiv.org/abs/1801.07632

Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

• arxiv: http://arxiv.org/abs/1608.03981
• github: https://github.com/cszn/DnCNN

Medical image denoising using convolutional denoising autoencoders

• arxiv: http://arxiv.org/abs/1608.04667

Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising

• arxiv: http://arxiv.org/abs/1609.03024

Non-Local Color Image Denoising with Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1611.06757

Joint Visual Denoising and Classification using Deep Learning

• intro: ICIP 2016
• arxiv: https://arxiv.org/abs/1612.01075
• github: https://github.com/ganggit/jointmodel

Deep Convolutional Denoising of Low-Light Images

• arxiv: https://arxiv.org/abs/1701.01687

Deep Class Aware Denoising

• arxiv: https://arxiv.org/abs/1701.01698

End-to-End Learning for Structured Prediction Energy Networks

• intro: University of Massachusetts & CMU
• arxiv: https://arxiv.org/abs/1703.05667

Block-Matching Convolutional Neural Network for Image Denoising

https://arxiv.org/abs/1704.00524

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

https://arxiv.org/abs/1706.04284

Wide Inference Network for Image Denoising

https://arxiv.org/abs/1707.05414

Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising

• arxiv: https://arxiv.org/abs/1707.09135
• github(MatConvNet): https://github.com/cswin/WIN

Image Denoising via CNNs: An Adversarial Approach

• intro: Indian Institute of Science
• arxiv: https://arxiv.org/abs/1708.00159

An ELU Network with Total Variation for Image Denoising

• intro: 24th International Conference on Neural Information Processing (2017)
• arxiv: https://arxiv.org/abs/1708.04317

Dilated Residual Network for Image Denoising

https://arxiv.org/abs/1708.05473

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

• arxiv: https://arxiv.org/abs/1710.04026
• github(MatConvNet): https://github.com/cszn/FFDNet

Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising

https://arxiv.org/abs/1711.07807

Burst Denoising with Kernel Prediction Networks

• project page: http://people.eecs.berkeley.edu/~bmild/kpn/
• arxiv: https://arxiv.org/abs/1712.02327

Chaining Identity Mapping Modules for Image Denoising

https://arxiv.org/abs/1712.02933

Deep Burst Denoising

https://arxiv.org/abs/1712.05790

Fast, Trainable, Multiscale Denoising

• intro: Google Research
• arxiv: https://arxiv.org/abs/1802.06130

Training Deep Learning based Denoisers without Ground Truth Data

https://arxiv.org/abs/1803.01314

Image Haze Removal

DehazeNet: An End-to-End System for Single Image Haze Removal

• arxiv: http://arxiv.org/abs/1601.07661

An All-in-One Network for Dehazing and Beyond

• intro: All-in-One Dehazing Network (AOD-Net)
• arxiv: https://arxiv.org/abs/1707.06543

Joint Transmission Map Estimation and Dehazing using Deep Networks

https://arxiv.org/abs/1708.00581

End-to-End United Video Dehazing and Detection

https://arxiv.org/abs/1709.03919

Image Dehazing using Bilinear Composition Loss Function

https://arxiv.org/abs/1710.00279

Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond

https://arxiv.org/abs/1711.06787

CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal

https://arxiv.org/abs/1801.02892

C2MSNet: A Novel approach for single image haze removal

• intro: WACV 2018
• arxiv: https://arxiv.org/abs/1801.08406

A Cascaded Convolutional Neural Network for Single Image Dehazing

• intro: IEEE ACCESS
• arxiv: https://arxiv.org/abs/1803.07955

Densely Connected Pyramid Dehazing Network

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.08396
• github: https://github.com/hezhangsprinter/DCPDN

Gated Fusion Network for Single Image Dehazing

• project page: https://sites.google.com/site/renwenqi888/research/dehazing/gfn
• arxiv: https://arxiv.org/abs/1804.00213

Semantic Single-Image Dehazing

https://arxiv.org/abs/1804.05624

Perceptually Optimized Generative Adversarial Network for Single Image Dehazing

https://arxiv.org/abs/1805.01084

PAD-Net: A Perception-Aided Single Image Dehazing Network

• arxiv: https://arxiv.org/abs/1805.03146
• github: https://github.com/guanlongzhao/single-image-dehazing

The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing

https://arxiv.org/abs/1805.03305

Image Rain Removal / De-raining

Clearing the Skies: A deep network architecture for single-image rain removal

• intro: DerainNet
• project page: http://smartdsp.xmu.edu.cn/derainNet.html
• arxiv: http://arxiv.org/abs/1609.02087
• code(Matlab): http://smartdsp.xmu.edu.cn/memberpdf/fuxueyang/derainNet/code.zip

Joint Rain Detection and Removal via Iterative Region Dependent Multi-Task Learning

• arxiv: http://arxiv.org/abs/1609.07769

Image De-raining Using a Conditional Generative Adversarial Network

• arxiv: https://arxiv.org/abs/1701.05957

Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1712.06830

Deep joint rain and haze removal from single images

https://arxiv.org/abs/1801.06769

Density-aware Single Image De-raining using a Multi-stream Dense Network

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.08396
• github: https://github.com/hezhangsprinter/DID-MDN

Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework

https://arxiv.org/abs/1803.10433

Fast Single Image Rain Removal via a Deep Decomposition-Composition Network

https://arxiv.org/abs/1804.02688

Residual-Guide Feature Fusion Network for Single Image Deraining

https://arxiv.org/abs/1804.07493

Fence Removal

Deep learning based fence segmentation and removal from an image using a video sequence

• intro: ECCV Workshop on Video Segmentation, 2016
• arxiv: http://arxiv.org/abs/1609.07727

Snow Removal

DesnowNet: Context-Aware Deep Network for Snow Removal

https://arxiv.org/abs/1708.04512

Blur Detection and Removal

Learning to Deblur

• arxiv: http://arxiv.org/abs/1406.7444

Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

• arxiv: http://arxiv.org/abs/1503.00593

End-to-End Learning for Image Burst Deblurring

• arxiv: http://arxiv.org/abs/1607.04433

Deep Video Deblurring

• intro: CVPR 2017 spotlight paper
• project page(code+dataset): http://www.cs.ubc.ca/labs/imager/tr/2017/DeepVideoDeblurring/
• arxiv: https://arxiv.org/abs/1611.08387 https://github.com/shuochsu/DeepVideoDeblurring

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

• arxiv: https://arxiv.org/abs/1612.02177
• github(official. Torch)): https://github.com/SeungjunNah/DeepDeblur_release

From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur

• arxiv: https://arxiv.org/abs/1612.02583

Motion Deblurring in the Wild

• arxiv: https://arxiv.org/abs/1701.01486

Deep Face Deblurring

https://arxiv.org/abs/1704.08772

Learning Blind Motion Deblurring

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.04208

Deep Generative Filter for Motion Deblurring

https://arxiv.org/abs/1709.03481

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

• intro: Ukrainian Catholic University & CTU in Prague
• arxiv: https://arxiv.org/abs/1711.07064
• github: https://github.com/KupynOrest/DeblurGAN

DeepDeblur: Fast one-step blurry face images restoration

https://arxiv.org/abs/1711.09515

Reblur2Deblur: Deblurring Videos via Self-Supervised Learning

• arxiv: https://arxiv.org/abs/1801.05117
• supplementary: https://drive.google.com/file/d/17Itta-z89lpWUdvUjpafKzJRSLoxHF5c/view

Scale-recurrent Network for Deep Image Deblurring

• intro: CUHK & Tecent & Megvii Inc.
• arxiv: https://arxiv.org/abs/1802.01770

Deep Semantic Face Deblurring

• intro: CVPR 2018. Beijing Institute of Technology & University of California, Merced & Nvidia Research
• project page: https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
• arxiv: https://arxiv.org/abs/1803.03345

Motion deblurring of faces

https://arxiv.org/abs/1803.03330

Learning a Discriminative Prior for Blind Image Deblurring

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.03363

Adversarial Spatio-Temporal Learning for Video Deblurring

https://arxiv.org/abs/1804.00533

Image Compression

An image compression and encryption scheme based on deep learning

• arxiv: http://arxiv.org/abs/1608.05001

Full Resolution Image Compression with Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1608.05148
• github: https://github.com/tensorflow/models/tree/master/compression

Image Compression with Neural Networks

• blog: https://research.googleblog.com/2016/09/image-compression-with-neural-networks.html

Lossy Image Compression With Compressive Autoencoders

• paper: http://openreview.net/pdf?id=rJiNwv9gg
• review: http://qz.com/835569/twitter-is-getting-close-to-making-all-your-pictures-just-a-little-bit-smaller/

End-to-end Optimized Image Compression

• arxiv: https://arxiv.org/abs/1611.01704
• notes: https://blog.acolyer.org/2017/05/08/end-to-end-optimized-image-compression/

CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

• arxiv: https://arxiv.org/abs/1611.07233

Semantic Perceptual Image Compression using Deep Convolution Networks

• intro: Accepted to Data Compression Conference
• intro: Semantic JPEG image compression using deep convolutional neural network (CNN)
• arxiv: https://arxiv.org/abs/1612.08712
• github: https://github.com/iamaaditya/image-compression-cnn

Generative Compression

• intro: MIT
• arxiv: https://arxiv.org/abs/1703.01467

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

https://arxiv.org/abs/1703.10114

Learning Convolutional Networks for Content-weighted Image Compression

https://arxiv.org/abs/1703.10553

Real-Time Adaptive Image Compression

• intro: ICML 2017
• keywords: GAN
• project page: http://www.wave.one/icml2017
• arxiv: https://arxiv.org/abs/1705.05823

Learning to Inpaint for Image Compression

https://arxiv.org/abs/1709.08855

Efficient Trimmed Convolutional Arithmetic Encoding for Lossless Image Compression

https://arxiv.org/abs/1801.04662

Conditional Probability Models for Deep Image Compression

https://arxiv.org/abs/1801.04260

Multiple Description Convolutional Neural Networks for Image Compression

https://arxiv.org/abs/1801.06611

Near-lossless L-infinity constrained Multi-rate Image Decompression via Deep Neural Network

https://arxiv.org/abs/1801.07987

DeepSIC: Deep Semantic Image Compression

https://arxiv.org/abs/1801.09468

Spatially adaptive image compression using a tiled deep network

• intro: ICIP 2017
• arxiv: https://arxiv.org/abs/1802.02629

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

• intro: IJCAI 2018
• arxiv: https://arxiv.org/abs/1803.05787

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

• intro: DAC 2018
• arxiv: https://arxiv.org/abs/1803.05788

The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples

https://arxiv.org/abs/1803.10418

Generative Adversarial Networks for Extreme Learned Image Compression

• intro: ETH Zurich
• homepage: https://data.vision.ee.ethz.ch/aeirikur/extremecompression/
• arxiv: https://arxiv.org/abs/1804.02958

Deformation Aware Image Compression

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.04593

Image Quality Assessment

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

• arxiv: https://arxiv.org/abs/1612.01697

Image Matting

Deep Image Matting

• intro: CVPR 2017
• intro: Beckman Institute for Advanced Science and Technology & Adobe Research
• arxiv: https://arxiv.org/abs/1703.03872

Fast Deep Matting for Portrait Animation on Mobile Phone

• intro: ACM Multimedia Conference (MM) 2017
• intro: does not need any interaction and can realize real-time matting with 15 fps
• arxiv: https://arxiv.org/abs/1707.08289

Real-time deep hair matting on mobile devices

• intro: ModiFace Inc, University of Toronto
• arxiv: https://arxiv.org/abs/1712.07168

TOM-Net: Learning Transparent Object Matting from a Single Image

• intro: CVPR 2018
• project page: http://gychen.org/TOM-Net/
• arxiv: https://arxiv.org/abs/1803.04636
• github: https://github.com/guanyingc/TOM-Net

Image Blending

GP-GAN: Towards Realistic High-Resolution Image Blending

• project page: https://wuhuikai.github.io/GP-GAN-Project/
• arxiv: https://arxiv.org/abs/1703.07195
• github(Official, Chainer): https://github.com/wuhuikai/GP-GAN

Image Enhancement

Deep Bilateral Learning for Real-Time Image Enhancement

• intro: MIT & Google Research
• arxiv: https://arxiv.org/abs/1707.02880

Aesthetic-Driven Image Enhancement by Adversarial Learning

• intro: CUHK
• arxiv: https://arxiv.org/abs/1707.05251

Learned Perceptual Image Enhancement

https://arxiv.org/abs/1712.02864

Abnormality Detection / Anomaly Detection

Toward a Taxonomy and Computational Models of Abnormalities in Images

• arxiv: http://arxiv.org/abs/1512.01325

Depth Prediction / Depth Estimation

Deep Convolutional Neural Fields for Depth Estimation from a Single Image

• intro: CVPR 2015
• arxiv: https://arxiv.org/abs/1411.6387

Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

• intro: IEEE T. Pattern Analysis and Machine Intelligence
• arxiv: https://arxiv.org/abs/1502.07411
• bitbucket: https://bitbucket.org/fayao/dcnf-fcsp

Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1603.04992
• github: https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation

Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions

• intro: NIPS 2016
• project pag: http://ttic.uchicago.edu/~ayanc/mdepth/
• arxiv: http://arxiv.org/abs/1605.07081
• github: https://github.com/ayanc/mdepth/

Deeper Depth Prediction with Fully Convolutional Residual Networks

• arxiv: https://arxiv.org/abs/1606.00373
• github: https://github.com/iro-cp/FCRN-DepthPrediction

Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference

https://arxiv.org/abs/1705.00534

Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference

• intro: Northwestern Polytechnical University
• arxiv: https://arxiv.org/abs/1708.02287

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

• arxiv: https://arxiv.org/abs/1709.07492
• video: https://www.youtube.com/watch?v=vNIIT_M7x7Y
• github: https://github.com/fangchangma/sparse-to-dense

Size-to-depth: A New Perspective for Single Image Depth Estimation

https://arxiv.org/abs/1801.04461

Texture Synthesis

Texture Synthesis Using Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1505.07376

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

• intro: IMCL 2016
• arxiv: http://arxiv.org/abs/1603.03417
• github: https://github.com/DmitryUlyanov/texture_nets
• notes: https://blog.acolyer.org/2016/09/23/texture-networks-feed-forward-synthesis-of-textures-and-stylized-images/

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

• arxiv: http://arxiv.org/abs/1604.04382
• github(Torch): https://github.com/chuanli11/MGANs

Texture Synthesis with Spatial Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1611.08207

Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

• intro: Skolkovo Institute of Science and Technology & Yandex & University of Oxford
• arxiv: https://arxiv.org/abs/1701.02096

Deep TEN: Texture Encoding Network

• intro: CVPR 2017
• project page: http://zhanghang1989.github.io/DeepEncoding/
• arxiv: https://arxiv.org/abs/1612.02844
• github: https://github.com/zhanghang1989/Deep-Encoding
• notes: https://zhuanlan.zhihu.com/p/25013378

Diversified Texture Synthesis with Feed-forward Networks

• intro: CVPR 2017. University of California & Adobe Research
• arxiv: https://arxiv.org/abs/1703.01664
• github: https://github.com/Yijunmaverick/MultiTextureSynthesis

Image Cropping

Deep Cropping via Attention Box Prediction and Aesthetics Assessment

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1710.08014

A2-RL: Aesthetics Aware Reinforcement Learning for Automatic Image Cropping

• intro: CVPR 2018
• project page: http://debangli.info/A2RL/
• arxiv: https://arxiv.org/abs/1709.04595
• github(official): https://github.com/wuhuikai/TF-A2RL
• demo: http://wuhuikai.me/TF-A2RL/

Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

• intro: IEEE Transactions on Multimedia, 2017
• arxiv: https://arxiv.org/abs/1712.09048

Image Synthesis

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

• arxiv: http://arxiv.org/abs/1601.04589

Generative Adversarial Text to Image Synthesis

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1605.05396
• github(Tensorflow): https://github.com/paarthneekhara/text-to-image

StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

• intro: Rutgers University & Lehigh University & The Chinese University of Hong Kong & University of North Carolina at Charlotte
• arxiv: https://arxiv.org/abs/1612.03242
• github: https://github.com/hanzhanggit/StackGAN
• github: https://github.com/brangerbriz/docker-StackGAN

Semantic Image Synthesis via Adversarial Learning

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1707.06873
• github(PyTorch): https://github.com//woozzu/dong_iccv_2017

An Introduction to Image Synthesis with Generative Adversarial Nets

• intro: University of Illinois at Chicago & Toutiao AI Lab
• arxiv: https://arxiv.org/abs/1803.04469

Image Tagging

Fast Zero-Shot Image Tagging

• project: http://crcv.ucf.edu/projects/fastzeroshot/

Flexible Image Tagging with Fast0Tag

• blog: https://gab41.lab41.org/flexible-image-tagging-with-fast0tag-681c6283c9b7

Sampled Image Tagging and Retrieval Methods on User Generated Content

• arxiv: https://arxiv.org/abs/1611.06962
• github: https://github.com/lab41/attalos

Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1711.06998

Deep Multiple Instance Learning for Zero-shot Image Tagging

https://arxiv.org/abs/1803.06051

Image Matching

Learning Fine-grained Image Similarity with Deep Ranking

• intro: CVPR 2014
• intro: Triplet Sampling
• arxiv: http://arxiv.org/abs/1404.4661

Learning to compare image patches via convolutional neural networks

• intro: CVPR 2015. siamese network
• project page: http://imagine.enpc.fr/~zagoruys/deepcompare.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zagoruyko_Learning_to_Compare_2015_CVPR_paper.pdf
• github: https://github.com/szagoruyko/cvpr15deepcompare

MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching

• intro: CVPR 2015. siamese network
• paper: http://www.cs.unc.edu/~xufeng/cs/papers/cvpr15-matchnet.pdf
• extended abstract: http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2A_114_ext.pdf
• github: https://github.com/hanxf/matchnet

Fashion Style in 128 Floats

• intro: CVPR 2016. StyleNet
• project page: http://hi.cs.waseda.ac.jp/~esimo/en/research/stylenet/
• paper: http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraCVPR2016.pdf
• github: https://github.com/bobbens/cvpr2016_stylenet

Fully-Trainable Deep Matching

• intro: BMVC 2016
• project page: http://lear.inrialpes.fr/src/deepmatching/
• arxiv: http://arxiv.org/abs/1609.03532

Local Similarity-Aware Deep Feature Embedding

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1610.08904

Convolutional neural network architecture for geometric matching

• intro: CVPR 2017. Inria
• project page: http://www.di.ens.fr/willow/research/cnngeometric/
• arxiv: https://arxiv.org/abs/1703.05593
• github: https://github.com/ignacio-rocco/cnngeometric_matconvnet

Multi-Image Semantic Matching by Mining Consistent Features

https://arxiv.org/abs/1711.07641

Image Editing

Neural Photo Editing with Introspective Adversarial Networks

• intro: Heriot-Watt University
• arxiv: http://arxiv.org/abs/1609.07093
• github: https://github.com/ajbrock/Neural-Photo-Editor

Deep Feature Interpolation for Image Content Changes

• intro: CVPR 2017. Cornell University & Washington University
• arxiv: https://arxiv.org/abs/1611.05507
• github(official): https://github.com/paulu/deepfeatinterp
• github: https://github.com/slang03/dfi-tensorflow

Invertible Conditional GANs for image editing

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1611.06355
• github: https://github.com/Guim3/IcGAN

Semantic Facial Expression Editing using Autoencoded Flow

• intro: University of Illinois at Urbana-Champaign & The Chinese University of Hong Kong & Google
• arxiv: https://arxiv.org/abs/1611.09961

Language-Based Image Editing with Recurrent Attentive Models

https://arxiv.org/abs/1711.06288

Face Swap & Face Editing

Fast Face-swap Using Convolutional Neural Networks

• intro: Ghent University & Twitter
• arxiv: https://arxiv.org/abs/1611.09577

Neural Face Editing with Intrinsic Image Disentangling

• intro: CVPR 2017 oral
• project page: http://www3.cs.stonybrook.edu/~cvl/content/neuralface/neuralface.html
• arxiv: https://arxiv.org/abs/1704.04131

RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces

https://arxiv.org/abs/1804.03447

FaceShop: Deep Sketch-based Face Image Editing

https://arxiv.org/abs/1804.08972

Stereo

End-to-End Learning of Geometry and Context for Deep Stereo Regression

https://arxiv.org/abs/1703.04309

Unsupervised Adaptation for Deep Stereo

• intro: ICCV 2017
• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Tonioni_Unsupervised_Adaptation_for_ICCV_2017_paper.pdf
• paper: http://vision.disi.unibo.it/~mpoggi/papers/iccv2017_adaptation.pdf
• github: https://github.com/CVLAB-Unibo/Unsupervised-Adaptation-for-Deep-Stereo

Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching

https://arxiv.org/abs/1708.09204

StereoConvNet: Stereo convolutional neural network for depth map prediction from stereo images

• github: https://github.com/LouisFoucard/StereoConvNet

EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching

https://arxiv.org/abs/1803.05196

Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains

• intro: CVPR 2018. SenseTime Research & Sun Yat-sen University
• arxiv: https://arxiv.org/abs/1803.06641

Pyramid Stereo Matching Network

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.08669
• github: https://github.com/JiaRenChang/PSMNet

Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching

https://arxiv.org/abs/1803.09437

Left-Right Comparative Recurrent Model for Stereo Matching

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.00796

Optical Flow

FlowNet: Learning Optical Flow with Convolutional Networks

• intro: “competitive accuracy at frame rates of 5 to 10 fps”
• project page: http://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15/
• arxiv: https://arxiv.org/abs/1504.06852
• github: https://github.com/ClementPinard/FlowNetTorch
• github: https://github.com/ClementPinard/FlowNetPytorch

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

• intro: CVPR 2017
• project page: http://lmb.informatik.uni-freiburg.de/Publications/2016/IMKDB16/
• arxiv: https://arxiv.org/abs/1612.01925
• github(Caffe): https://github.com/lmb-freiburg/flownet2
• github: https://github.com//NVIDIA/flownet2-pytorch
• video: http://lmb.informatik.uni-freiburg.de/Publications/2016/IMKDB16/

Optical Flow Estimation using a Spatial Pyramid Network

• arxiv: https://arxiv.org/abs/1611.00850

Guided Optical Flow Learning

• arxiv: https://arxiv.org/abs/1702.02295

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

https://arxiv.org/abs/1709.02371

Occlusion Aware Unsupervised Learning of Optical Flow

https://arxiv.org/abs/1711.05890

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1711.07837

Learning Optical Flow via Dilated Networks and Occlusion Reasoning

• intro: ICIP 2018
• arxiv: https://arxiv.org/abs/1805.02733

3D

Learning Spatiotemporal Features with 3D Convolutional Networks

C3D: Generic Features for Video Analysis

• project page: http://vlg.cs.dartmouth.edu/c3d/
• arxiv: http://arxiv.org/abs/1412.0767
• slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w07-conv3d.pdf
• github: https://github.com/facebook/C3D

C3D Model for Keras trained over Sports 1M

• project page: https://imatge.upc.edu/web/resources/c3d-model-keras-trained-over-sports-1m

Sports 1M C3D Network to Keras

• notebook: http://nbviewer.jupyter.org/gist/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2/Sports1M%20C3D%20Network%20to%20Keras.ipynb

Deep End2End Voxel2Voxel Prediction

• arxiv: http://arxiv.org/abs/1511.06681

Aligning 3D Models to RGB-D Images of Cluttered Scenes

• paper: http://www.cs.berkeley.edu/~rbg/papers/cvpr15/align2rgbd.pdf

Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images

• homepage: http://dss.cs.princeton.edu/
• arxiv: http://arxiv.org/abs/1511.02300

Multi-view 3D Models from Single Images with a Convolutional Network

• arxiv: http://arxiv.org/abs/1511.06702

RotationNet: Learning Object Classification Using Unsupervised Viewpoint Estimation

• arxiv: http://arxiv.org/abs/1603.06208

DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

• paper: http://deepcontext.cs.princeton.edu/paper.pdf
• project page: http://deepcontext.cs.princeton.edu/

Volumetric and Multi-View CNNs for Object Classification on 3D Data

• homepage: http://graphics.stanford.edu/projects/3dcnn/
• arxiv: https://arxiv.org/abs/1604.03265
• github: https://github.com/charlesq34/3dcnn.torch

Deep3D: Automatic 2D-to-3D Video Conversion with CNNs

• project page: http://dmlc.ml/mxnet/2016/04/04/deep3d-automatic-2d-to-3d-conversion-with-CNN.html
• paper: http://homes.cs.washington.edu/~jxie/pdf/deep3d.pdf
• github: https://github.com/piiswrong/deep3d

Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1604.03650

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

• arxiv: http://arxiv.org/abs/1604.00449
• github: https://github.com/chrischoy/3D-R2N2

Deep Learning for Makeup

Makeup like a superstar: Deep Localized Makeup Transfer Network

• intro: IJCAI 2016
• arxiv: http://arxiv.org/abs/1604.07102

Makeup-Go: Blind Reversion of Portrait Edit

• intro: The Chinese University of Hong Kong & Tencent Youtu Lab
• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Makeup-Go_Blind_Reversion_ICCV_2017_paper.pdf
• paper: http://open.youtu.qq.com/content/file/iccv17_makeupgo.pdf

Music Tagging

Automatic tagging using deep convolutional neural networks

• arxiv: https://arxiv.org/abs/1606.00298
• github: https://github.com/keunwoochoi/music-auto_tagging-keras

Music tagging and feature extraction with MusicTaggerCRNN

https://keras.io/applications/#music-tagging-and-feature-extraction-with-musictaggercrnn

Action Recognition

Single Image Action Recognition by Predicting Space-Time Saliency

https://arxiv.org/abs/1705.04641

Attentional Pooling for Action Recognition

• intro: NIPS 2017
• project page: https://rohitgirdhar.github.io/AttentionalPoolingAction/
• arxiv: https://arxiv.org/abs/1711.01467
• github: https://github.com/rohitgirdhar/AttentionalPoolingAction/

Memory Attention Networks for Skeleton-based Action Recognition

• intro: IJCAI 2018
• keywords: Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM)
• arixv: https://arxiv.org/abs/1804.08254
• github: https://github.com/memory-attention-networks

CTR Prediction

Deep CTR Prediction in Display Advertising

• intro: ACM Multimedia Conference 2016
• arxiv: https://arxiv.org/abs/1609.06018

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

• intro: Harbin Institute of Technology & Huawei
• arxiv: https://arxiv.org/abs/1703.04247

Deep Interest Network for Click-Through Rate Prediction

• intro: Alibaba Inc.
• arxiv: https://arxiv.org/abs/1706.06978

Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior

• intro: Alibaba Inc.
• arxiv: https://arxiv.org/abs/1711.06505

Cryptography

Learning to Protect Communications with Adversarial Neural Cryptography

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1610.06918
• github(Theano): https://github.com/nlml/adversarial-neural-crypt
• github(TensorFlow): https://github.com/ankeshanand/neural-cryptography-tensorflow

Adversarial Neural Cryptography in Theano

• blog: https://nlml.github.io/neural-networks/adversarial-neural-cryptography/

Embedding Watermarks into Deep Neural Networks

• arxiv: https://arxiv.org/abs/1701.04082
• github: https://github.com/yu4u/dnn-watermark

Digital Watermarking for Deep Neural Networks

• intro: International Journal of Multimedia Information Retrieval
• arxiv: https://arxiv.org/abs/1802.02601

Cyber Security

Collection of Deep Learning Cyber Security Research Papers

• blog: https://medium.com/@jason_trost/collection-of-deep-learning-cyber-security-research-papers-e1f856f71042#.fcus2cu9m

Lip Reading

LipNet: Sentence-level Lipreading

LipNet: End-to-End Sentence-level Lipreading

• arxiv: https://arxiv.org/abs/1611.01599
• paper: http://openreview.net/pdf?id=BkjLkSqxg
• github: https://github.com/bshillingford/LipNet

Lip Reading Sentences in the Wild

• intro: University of Oxford & Google DeepMind
• arxiv: https://arxiv.org/abs/1611.05358
• youtube: https://www.youtube.com/watch?v=5aogzAUPilE

Combining Residual Networks with LSTMs for Lipreading

• arxiv: https://arxiv.org/abs/1703.04105

End-to-End Multi-View Lipreading

• intro: BMVC 2017
• arxiv: https://arxiv.org/abs/1709.00443

LCANet: End-to-End Lipreading with Cascaded Attention-CTC

• intro: FG 2018
• arxiv: https://arxiv.org/abs/1803.04988

Event Recognition

Better Exploiting OS-CNNs for Better Event Recognition in Images

• arxiv: http://arxiv.org/abs/1510.03979

Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images

• arxiv: http://arxiv.org/abs/1609.00162

IOD-CNN: Integrating Object Detection Networks for Event Recognition

https://arxiv.org/abs/1703.07431

Deep Learning in Finance

Deep Learning in Finance

• arxiv: http://arxiv.org/abs/1602.06561

A Survey of Deep Learning Techniques Applied to Trading

• blog: http://gregharris.info/a-survey-of-deep-learning-techniques-applied-to-trading/

Deep Learning and Long-Term Investing

• part 1: http://www.euclidean.com/deep-learning-long-term-investing-1
• part 2: http://www.euclidean.com/deep-learning-investing-part-2-preprocessing-data

Deep Learning in Trading

• youtube: https://www.youtube.com/watch?v=FoQKCeDuPiY
• mirror: https://pan.baidu.com/s/1sltRra9

Research to Products: Machine & Human Intelligence in Finance

• intro: Peter Sarlin, Hanken School of Economics - Deep Learning in Finance Summit 2016 #reworkfin
• youtube: https://www.youtube.com/watch?v=Fd7Cc-KOVXg
• mirror: https://pan.baidu.com/s/1kVpZKur#list/path=%2F

eep Neural Networks for Real-time Market Predictions

• youtube: https://www.youtube.com/watch?v=Kzz2-wAEK7A

Deep Learning the Stock Market

• blog: https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02#.z752rf43u
• github: https://github.com/talolard/MarketVectors

rl_portfolio

• intro: This Repository uses Reinforcement Learning and Supervised learning to Optimize portfolio allocation.
• github: https://github.com/deependersingla/deep_portfolio

Neural networks for algorithmic trading. Multivariate time series

• blog: https://medium.com/@alexrachnog/neural-networks-for-algorithmic-trading-2-1-multivariate-time-series-ab016ce70f57
• github: https://github.com/Rachnog/Deep-Trading/tree/master/multivariate

Deep-Trading: Algorithmic trading with deep learning experiments

https://github.com/Rachnog/Deep-Trading

Neural networks for algorithmic trading. Multimodal and multitask deep learning

• blog: https://becominghuman.ai/neural-networks-for-algorithmic-trading-multimodal-and-multitask-deep-learning-5498e0098caf
• github: https://github.com/Rachnog/Deep-Trading/tree/master/multimodal

Deep Learning with Python in Finance - Singapore Python User Group

• youtube: https://www.youtube.com/watch?v=xvm-M-R2fZY

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

• intro: Xi’an Jiaotong-Liverpool University
• keywords: PGPortfolio: Policy Gradient Portfolio
• arxiv: https://arxiv.org/abs/1706.10059
• github: https://github.com//ZhengyaoJiang/PGPortfolio

Stock Prediction: a method based on extraction of news features and recurrent neural networks

• intro: Peking University. The 22nd China Conference on Information Retrieval
• arxiv: https://arxiv.org/abs/1707.07585

Multidimensional LSTM Networks to Predict Bitcoin Price

• blog: http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price
• github: https://github.com/jaungiers/Multidimensional-LSTM-BitCoin-Time-Series

Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals

• intro: Euclidean Technologies & Amazon AI
• arxiv: https://arxiv.org/abs/1711.04837

Findings from our Research on Applying Deep Learning to Long-Term Investing

http://www.euclidean.com/paper-on-deep-learning-long-term-investing

Predicting Cryptocurrency Prices With Deep Learning

• intro: This post brings together cryptos and deep learning in a desperate attempt for Reddit popularity
• blog: https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/

Deep Learning in Speech

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

• intro: Baidu Research, ICML 2016
• arxiv: https://arxiv.org/abs/1512.02595
• github(Neon): https://github.com/NervanaSystems/deepspeech

End-to-end speech recognition with neon

• blog: https://www.nervanasys.com/end-end-speech-recognition-neon/

WaveNet

WaveNet: A Generative Model for Raw Audio

• homepage: https://deepmind.com/blog/wavenet-generative-model-raw-audio/
• paper: https://drive.google.com/file/d/0B3cxcnOkPx9AeWpLVXhkTDJINDQ/view
• mirror: https://pan.baidu.com/s/1gfmGWaJ
• github: https://github.com/usernaamee/keras-wavenet
• github: https://github.com/ibab/tensorflow-wavenet
• github: https://github.com/monthly-hack/chainer-wavenet
• github: https://github.com/huyouare/WaveNet-Theano
• github(Keras): https://github.com/basveeling/wavenet
• github: https://github.com/ritheshkumar95/WaveNet

A TensorFlow implementation of DeepMind’s WaveNet paper for text generation.

• github: https://github.com/Zeta36/tensorflow-tex-wavenet

Fast Wavenet Generation Algorithm

• intro: An efficient Wavenet generation implementation
• arxiv: https://arxiv.org/abs/1611.09482
• github https://github.com/tomlepaine/fast-wavenet

Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition based on DeepMind’s WaveNet and tensorflow

• github: https://github.com/buriburisuri/speech-to-text-wavenet

Wav2Letter: an End-to-End ConvNet-based Speech Recognition System

• arxiv: http://arxiv.org/abs/1609.03193

TristouNet: Triplet Loss for Speaker Turn Embedding

• arxiv: https://arxiv.org/abs/1609.04301
• github: https://github.com/hbredin/TristouNet

Speech Recognion and Deep Learning

• intro: Baidu Research Silicon Valley AI Lab
• slides: http://cs.stanford.edu/~acoates/ba_dls_speech2016.pdf
• mirror: https://pan.baidu.com/s/1qYrPkPQ
• github: https://github.com/baidu-research/ba-dls-deepspeech

Robust end-to-end deep audiovisual speech recognition

• intro: CMU
• github: https://arxiv.org/abs/1611.06986

An Experimental Comparison of Deep Neural Networks for End-to-end Speech Recognition

• arxiv: https://arxiv.org/abs/1611.07174

Recurrent Deep Stacking Networks for Speech Recognition

• intro: The Ohio State University
• arxiv: https://arxiv.org/abs/1612.04675

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

• intro: Universite de Montreal & CIFAR
• arxiv: https://arxiv.org/abs/1701.02720

Deep Learning for Sound / Music

Sound

Suggesting Sounds for Images from Video Collections

• intro: ETH Zurich & 2Disney Research
• paper: https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20161014182443/Suggesting-Sounds-for-Images-from-Video-Collections-Paper.pdf

Disney AI System Associates Images with Sounds

• blog: https://news.developer.nvidia.com/disneys-ai-system-associates-images-with-sounds/

Convolutional Recurrent Neural Networks for Bird Audio Detection

https://arxiv.org/abs/1703.02317

Visual to Sound: Generating Natural Sound for Videos in the Wild

• project page: http://bvision11.cs.unc.edu/bigpen/yipin/visual2sound_webpage/visual2sound.html
• arxiv: https://arxiv.org/abs/1712.01393

Music

Learning Features of Music from Scratch

• intro: University of Washington. MusicNet
• project page: http://homes.cs.washington.edu/~thickstn/musicnet.html
• arxiv: https://arxiv.org/abs/1611.09827
• demo: http://homes.cs.washington.edu/~thickstn/demos.html

DeepBach: a Steerable Model for Bach chorales generation

• project page: http://www.flow-machines.com/deepbach-steerable-model-bach-chorales-generation/
• arxiv: https://arxiv.org/abs/1612.01010
• github: https://github.com/SonyCSL-Paris/DeepBach
• youtube: https://www.youtube.com/watch?v=QiBM7-5hA6o

Deep Learning for Music

• blog: https://amundtveit.com/2016/11/22/deep-learning-for-music/

First International Workshop on Deep Learning and Music

https://arxiv.org/html/1706.08675

Deep Learning in Medicine and Biology

Low Data Drug Discovery with One-shot Learning

• intro: MIT & Stanford University
• arxiv: https://arxiv.org/abs/1611.03199
• homepage: http://deepchem.io/
• github: https://github.com/deepchem/deepchem

Democratizing Drug Discovery with DeepChem

• youtube: https://www.youtube.com/watch?v=sntikyFI8s8

Introduction to Deep Learning in Medicine and Biology

• blog: http://a12d.com/deep-learning-biomedicine

Deep Learning for Alzheimer Diagnostics and Decision Support

https://amundtveit.com/2016/11/18/deep-learning-for-alzheimer-diagnostics-and-decision-support/

DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning

• intro: University of Florida
• arxiv: https://arxiv.org/abs/1612.03211

Towards biologically plausible deep learning

• intro: Yoshua Bengio, NIPS’2016 Workshops
• slides: http://www.iro.umontreal.ca/~bengioy/talks/Brains+Bits-NIPS2016Workshop.pptx.pdf

Deep Learning and Its Applications to Machine Health Monitoring: A Survey

• arxiv: https://arxiv.org/abs/1612.07640

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1701.01329

Deep Learning Applications in Medical Imaging

• blog: http://techemergence.com/deep-learning-medical-applications/

Dermatologist-level classification of skin cancer with deep neural networks

• intro: Stanford University. Nature 2017
• paper: http://www.nature.com/nature/journal/vaop/ncurrent/pdf/nature21056.pdf

Deep Learning for Health Informatics

• intro: Imperial College London
• paper: http://ieeexplore.ieee.org/abstract/document/7801947/

Deep Learning for Fashion

Convolutional Neural Networks for Fashion Classification and Object Detection

• intro: CS231N project
• paper: http://cs231n.stanford.edu/reports/BLAO_KJAG_CS231N_FinalPaperFashionClassification.pdf

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

• intro: CVPR 2016
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf

Deep Learning for Fast and Accurate Fashion Item Detection

• keywords: MultiBox and Fast R-CNN, Kuznech-Fashion-156 and Kuznech-Fashion-205 fashion item detection datasets
• paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf

Deep Learning at GILT

• keywords: automated tagging, automatic dress faceting
• blog: http://tech.gilt.com/machine/learning,/deep/learning/2016/12/22/deep-learning-at-gilt

Working with Fashion Models

• blog: https://making.lyst.com/2017/02/21/working-with-fashion-models/
• youtube: https://www.youtube.com/watch?v=emr2qaCQOQs

Fashion Forward: Forecasting Visual Style in Fashion

• intro: Karlsruhe Institute of Technology & The University of Texas at Austin
• arxiv: https://arxiv.org/abs/1705.06394

StreetStyle: Exploring world-wide clothing styles from millions of photos

• homepage: http://streetstyle.cs.cornell.edu/
• arxiv: https://arxiv.org/abs/1706.01869
• demo: http://streetstyle.cs.cornell.edu/trends.html

Fashioning with Networks: Neural Style Transfer to Design Clothes

• intro: ML4Fashion 2017
• arxiv: https://arxiv.org/abs/1707.09899

Deep Learning Our Way Through Fashion Week

https://inside.edited.com/deep-learning-our-way-through-fashion-week-ea55bf50bab8

Be Your Own Prada: Fashion Synthesis with Structural Coherence

• intro: ICCV 2017
• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Be_Your_Own_ICCV_2017_paper.pdf
• github: https://github.com/zhusz/ICCV17-fashionGAN

Others

Selfai: Predicting Facial Beauty in Selfies

Selfai: A Method for Understanding Beauty in Selfies

• blog: http://www.erogol.com/selfai-predicting-facial-beauty-selfies/
• github: https://github.com/erogol/beauty.torch

Deep Learning Enables You to Hide Screen when Your Boss is Approaching

• blog: http://ahogrammer.com/2016/11/15/deep-learning-enables-you-to-hide-screen-when-your-boss-is-approaching/
• github: https://github.com/Hironsan/BossSensor

Blogs

40 Ways Deep Learning is Eating the World

https://medium.com/intuitionmachine/the-ultimate-deep-learning-applications-list-434d1425da1d#.rxq8xvbfz

Applications

http://www.deeplearningpatterns.com/doku.php/applications

Systematic Approach To Applications Of Deep Learning

https://gettocode.com/2016/11/25/systematic-approach-to-applications-of-deep-learning/

Resources

Deep Learning Gallery - a curated collection of deep learning projects

http://deeplearninggallery.com/