Fun With Deep Learning

Painting

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

• intro: ICML 2012
• arxiv: https://arxiv.org/abs/1206.4634

Neural Art

A Neural Algorithm of Artistic Style

• arxiv: http://arxiv.org/abs/1508.06576
• gitxiv: http://gitxiv.com/posts/jG46ukGod8R7Rdtud/a-neural-algorithm-of-artistic-style
• github: https://github.com/kaishengtai/neuralart
• github: https://github.com/jcjohnson/neural-style
• github: https://github.com/andersbll/neural_artistic_style
• ipn: http://nbviewer.ipython.org/github/Lasagne/Recipes/blob/master/examples/styletransfer/Art%20Style%20Transfer.ipynb
• github: https://github.com/mbartoli/neural-animation
• github: https://github.com/memisevic/artify
• github: https://github.com/mattya/chainer-gogh
• github(TensorFlow): https://github.com/anishathalye/neural-style
• github: https://github.com/woodrush/neural-art-tf
• github: https://github.com/dmlc/mxnet/tree/master/example/neural-style
• demo: http://deepart.io/
• github: https://github.com/Teaonly/easyStyle
• github: https://github.com/ckmarkoh/neuralart_tensorflow
• github: https://github.com/fzliu/style-transfer
• github: https://github.com/titu1994/Neural-Style-Transfer
• github: https://github.com/saikatbsk/Vincent-AI-Artist
• github: https://github.com/zhaw/neural_style

Image Style Transfer Using Convolutional Neural Networks

• paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf

Artificial Startup Style: Neural art about startup fashion

• blog: https://medium.com/data-engineering/artificial-startup-style-437f6090b1f7#.8u06gq42e

From Pixels to Paragraphs: How artistic experiments with deep learning guard us from hype

• blog: https://medium.com/@genekogan/from-pixels-to-paragraphs-eb2763da0e9b#.er3djn9z9

Experiments with style transfer

• website: http://mtyka.github.io/code/2015/10/02/experiments-with-style-transfer.html

Style Transfer for Headshot Portraits (SIGGRAPH 2014)

• project: https://people.csail.mit.edu/yichangshih/portrait_web/

Teaching recurrent Neural Networks about Monet

• blog: http://blog.manugarri.com/teaching-recurrent-neural-networks-about-monet/
• github: https://github.com/manugarri/keras_monet

Content Aware Neural Style Transfer

• arxiv: http://arxiv.org/abs/1601.04568

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

• arxiv: http://arxiv.org/abs/1601.04589
• github: https://github.com/chuanli11/CNNMRF

Stylenet: Neural Network with Style Synthesis

• github: https://github.com/machrisaa/stylenet

Ostagram

• intro: This program presents web-service for algorithm combining the content of one image with the style of another image using convolutional neural networks
• github: https://github.com/SergeyMorugin/ostagram

Exploring the Neural Algorithm of Artistic Style

• intro: A short class project report
• arxiv: http://arxiv.org/abs/1602.07188

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

• intro: Justin Johnson, Alexandre Alahi, Li Fei-Fei. ECCV 2016
• arxiv: http://arxiv.org/abs/1603.08155
• github: https://github.com/jcjohnson/fast-neural-style
• github: https://github.com/yusuketomoto/chainer-fast-neuralstyle
• github: https://github.com/awentzonline/keras-rtst
• github(Keras): https://github.com/titu1994/Fast-Neural-Style
• gtihub(Tensorflow): [https://github.com/junrushao1994/fast-neural-style.tf] (https://github.com/junrushao1994/fast-neural-style.tf)
• github(PyTorch): https://github.com/bengxy/FastNeuralStyle

Image transformation networks with fancy loss functions

• intro: Fast neural style in tensorflow based on http://arxiv.org/abs/1603.08155
• blog: http://olavnymoen.com/2016/07/07/image-transformation-network
• github: https://github.com/OlavHN/fast-neural-style

Improving the Neural Algorithm of Artistic Style

• arxiv: http://arxiv.org/abs/1605.04603

CubistMirror: an openframeworks app which repeatedly applies real-time style transfer on a webcam

• github: https://github.com/genekogan/CubistMirror

Transfer Style But Not Color

• blog: http://blog.deepart.io/2016/06/04/color-independent-style-transfer/
• github: https://github.com/pavelgonchar/color-independent-style-transfer

neural-art-mini: Lightweight version of mxnet neural art implementation

• intro: Lightweight version of mxnet neural art implementation using ~4.8M SqueezeNet model. Compressed model is less than 500KB
• github: https://github.com/pavelgonchar/neural-art-mini

Preserving Color in Neural Artistic Style Transfer

• arxiv: http://arxiv.org/abs/1606.05897

End to End Neural Art with Generative Models

• blog: http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html
• github: https://github.com/dmlc/mxnet/tree/master/example/neural-style

Neural Style Explained

• blog: http://kvfrans.com/neural-style-explained/
• github: https://github.com/kvfrans/neural-style

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/
• github: https://github.com/tgyg-jegli/tf_texture_net

Learning Typographic Style

• arxiv: https://arxiv.org/abs/1603.04000

Instance Normalization: The Missing Ingredient for Fast Stylization

• arxiv: http://arxiv.org/abs/1607.08022

Painting style transfer for head portraits using convolutional neural networks

• paper: http://dl.acm.org/citation.cfm?id=2925968
• sci-hub: http://dl.acm.org.sci-hub.cc/citation.cfm?doid=2897824.2925968

Style-Transfer via Texture-Synthesis

• arxiv: http://arxiv.org/abs/1609.03057

neural-style-tf: TensorFlow implementation of Neural Style

• github: https://github.com/cysmith/neural-style-tf

Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

• intro: In Proceedings of the 7th International Conference on Computational Creativity. Palo Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press (2016)
• arxiv: https://arxiv.org/abs/1610.02478

A Learned Representation For Artistic Style

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1610.07629
• blog: https://research.googleblog.com/2016/10/supercharging-style-transfer.html
• github: https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization
• github(Tensorflow): https://github.com/Heumi/Fast_Multi_Style_Transfer-tf

How to Fake It As an Artist with Docker, AWS and Deep Learning

• blog: https://medium.com/@lherrera/how-to-fake-it-as-an-artist-with-docker-aws-and-deep-learning-6d42f4acd890#.eq9gcgkzh

Multistyle Pastiche Generator

• blog: https://magenta.tensorflow.org/2016/11/01/multistyle-pastiche-generator/

Fast Style Transfer in TensorFlow

• intro: Video Stylization, Image Stylization
• github: https://github.com/lengstrom/fast-style-transfer

Neural Style Transfer For Chinese Fonts

• github: https://github.com/kaonashi-tyc/Rewrite

Neural Style Representations and the Large-Scale Classification of Artistic Style

• arxiv: https://arxiv.org/abs/1611.05368

Controlling Perceptual Factors in Neural Style Transfer

• intro: University of Tubingen & Adobe Research
• arxiv: https://arxiv.org/abs/1611.07865

Awesome Typography: Statistics-Based Text Effects Transfer

• arxiv: https://arxiv.org/abs/1611.09026

Fast Patch-based Style Transfer of Arbitrary Style

• arxiv: https://arxiv.org/abs/1612.04337
• github: https://github.com/rtqichen/style-swap

Demystifying Neural Style Transfer

• intro: IJCAI 2017
• arxiv: https://arxiv.org/abs/1701.01036
• github: https://github.com/lyttonhao/Neural-Style-MMD

Son of Zorn’s Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

• arxiv: https://arxiv.org/abs/1701.02357

Bringing Impressionism to Life with Neural Style Transfer in Come Swim

• intro: a case study of how Neural Style Transfer can be used in a movie production context
• keywords: Kristen Stewart !
• arxiv: https://arxiv.org/abs/1701.04928

Pytorch tutorials for Neural Style transfert

• github: https://github.com/alexis-jacq/Pytorch-Tutorials

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

• arxiv: https://arxiv.org/abs/1701.08893

Arbitrary Style Transfer In Real-Time With Adaptive Instance Normalization

• intro: ICCV 2017. Cornell University
• paper: https://openreview.net/pdf?id=B1fUVMzKg
• github(Torch): https://github.com/xunhuang1995/AdaIN-style
• github(TensorFlow): https://github.com/elleryqueenhomels/arbitrary_style_transfer

Picking an optimizer for Style Transfer

• blog: https://medium.com/slavv/picking-an-optimizer-for-style-transfer-86e7b8cba84b#.cgv2oreaq
• github: https://github.com/slavivanov/Style-Tranfer

Multi-style Generative Network for Real-time Transfer

https://arxiv.org/abs/1703.06953

Deep Photo Style Transfer

• arxiv: https://arxiv.org/abs/1703.07511
• github(Torch): https://github.com/luanfujun/deep-photo-styletransfer
• github(Docker): https://github.com/martinbenson/deep-photo-styletransfer

Lightweight Neural Style on Pytorch

https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch

StyleBank: An Explicit Representation for Neural Image Style Transfer

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1703.09210

How to Make an Image More Memorable? A Deep Style Transfer Approach

• intro: ACM ICMR 2017
• arxiv: https://arxiv.org/abs/1704.01745

Visual Attribute Transfer through Deep Image Analogy

• intro: SIGGRAPH 2017
• keywords: Deep Image Analogy
• arxiv: https://arxiv.org/abs/1705.01088
• github: https://github.com/msracver/Deep-Image-Analogy

Characterizing and Improving Stability in Neural Style Transfer

https://arxiv.org/abs/1705.02092

Towards Metamerism via Foveated Style Transfer

https://arxiv.org/abs/1705.10041

Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

https://arxiv.org/abs/1706.03319

Meta Networks for Neural Style Transfer

• arxiv: https://arxiv.org/abs/1709.04111
• github: https://github.com/FalongShen/styletransfer

Neural Color Transfer between Images

• intro: Hong Kong University of Science and Technology & Microsoft Research
• arxiv: https://arxiv.org/abs/1710.00756

Improved Style Transfer by Respecting Inter-layer Correlations

https://arxiv.org/abs/1801.01933

Face Destylization

https://arxiv.org/abs/1802.01237

Unsupervised Typography Transfer

https://arxiv.org/abs/1802.02595

Stereoscopic Neural Style Transfer

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.10591

Arbitrary Style Transfer with Deep Feature Reshuffle

https://arxiv.org/abs/1805.04103

Neural Art On Audio

MSc AI Project on generative deep networks and neural style transfer for audio

• github: https://github.com/Fr-d-rik/generative_audio
• project report: https://github.com/Fr-d-rik/generative_audio/blob/master/docs/project_report.pdf

Neural Song Style

• intro: Audio style transfer AI
• github: https://github.com/rupeshs/neuralsongstyle

Time Domain Neural Audio Style Transfer

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1711.11160
• github: https://github.com//pkmital/time-domain-neural-audio-style-transfer

Neural Art On Video

neural-style-video

• blog: http://larseidnes.com/2015/12/18/painting-videos-with-neural-networks/
• github: https://github.com/larspars/neural-style-video

Instructions for making a Neural-Style movie

• website: https://gist.github.com/genekogan/d61c8010d470e1dbe15d
• sample: https://vimeo.com/139123754

Artistic style transfer for videos

• arxiv: http://arxiv.org/abs/1604.08610
• github: https://github.com/manuelruder/artistic-videos

Artistic style transfer for videos and spherical images

https://arxiv.org/abs/1708.04538

How Deep Learning Can Paint Videos in the Style of Art’s Great Masters

• blog: https://blogs.nvidia.com/blog/2016/05/25/deep-learning-paints-videos/

DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies

• arxiv: http://arxiv.org/abs/1605.08153

Coherent Online Video Style Transfer

https://arxiv.org/abs/1703.09211

Laplacian-Steered Neural Style Transfer

• intro: ACM Multimedia Conference (MM) 2017
• arxiv: https://arxiv.org/abs/1707.01253

Real-Time Neural Style Transfer for Videos

• intro: Tsinghua University & Tencent AI Lab
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Real-Time_Neural_Style_CVPR_2017_paper.pdf

Multi-Content GAN for Few-Shot Font Style Transfer

https://arxiv.org/abs/1712.00516

Neural Doodle

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

• paper: http://nucl.ai/semantic-style-transfer.pdf
• reddit: https://www.reddit.com/r/MachineLearning/comments/48zstj/my_wip_implementation_of_neural_image_analogies/
• github: https://github.com/alexjc/neural-doodle

Neural Doodle

• github: https://github.com/alexjc/neural-doodle

Faster neural doodle

• github: https://github.com/DmitryUlyanov/fast-neural-doodle

Feed-forward neural doodle

• blog: http://dmitryulyanov.github.io/feed-forward-neural-doodle/
• github: https://github.com/DmitryUlyanov/online-neural-doodle
• demo: http://likemo.net/

neural image analogies: Generate image analogies using neural matching and blending

• github: https://github.com/awentzonline/image-analogies

Neural doodle with Keras

https://github.com/fchollet/keras/blob/master/examples/neural_doodle.py

Deep Dreams

deepdream

• github: https://github.com/google/deepdream

cnn-vis: Use CNNs to generate images

• github: https://github.com/jcjohnson/cnn-vis

bat-country: A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs

• github: https://github.com/jrosebr1/bat-country

DeepDreaming with TensorFlow

• ipn: http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb

deepdraw

• github: https://github.com/auduno/deepdraw

Understanding Deep Dreams

• blog: http://www.alanzucconi.com/2015/07/06/live-your-deepdream-how-to-recreate-the-inceptionism-effect/

Generating Deep Dreams

• blog：http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/

Audio Deepdream: Optimizing Raw Audio With Convolutional Networks

• intro: Google Brain
• paper: https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/08/ardila-audio.pdf
• examples: https://drive.google.com/drive/folders/0B7NUtSSG_AgqZWF1ajdhSjhEMlk

Emoji

Brewing EmojiNet

• blog: http://engineering.curalate.com/2016/01/20/emojinet.html
• website: https://emojini.curalate.com/

Image2Emoji: Zero-shot Emoji Prediction for Visual Media

• paper: http://isis-data.science.uva.nl/cgmsnoek/pub/cappallo-image2emoji-mm2015.pdf

Teaching Robots to Feel: Emoji & Deep Learning 👾 💭 💕

• blog: http://getdango.com/emoji-and-deep-learning.html
• app: https://play.google.com/store/apps/details?id=co.dango.emoji.gif

Text input with relevant emoji sorted with deeplearning

• homepage: http://codepen.io/Idlework/pen/xOgGqM

Sketch

Sketch-a-Net that Beats Humans

• project page: http://www.eecs.qmul.ac.uk/~tmh/downloads.html
• arxiv: http://arxiv.org/abs/1501.07873
• paper: http://www.eecs.qmul.ac.uk/~tmh/papers/yu2015sketchanet.pdf
• code: http://www.eecs.qmul.ac.uk/~tmh/downloads/SketchANet_Code.zip

How Do Humans Sketch Objects?

• project page: http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/
• paper: http://cybertron.cg.tu-berlin.de/eitz/pdf/2012_siggraph_classifysketch.pdf
• github: https://github.com/Zebreu/SketchingAI
• gitxiv: http://gitxiv.com/posts/ZBCxEc9g3Fg5xCQ6n/sketchingai

Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup (SIGGRAPH 2016)

• homepage: http://hi.cs.waseda.ac.jp/~esimo/en/research/sketch/
• paper: http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraSIGGRAPH2016.pdf

Convolutional Sketch Inversion

• arxiv: http://arxiv.org/abs/1606.03073
• review: https://www.technologyreview.com/s/601684/machine-vision-algorithm-learns-to-transform-hand-drawn-sketches-into-photorealistic-images/
• review: https://techcrunch.com/2016/07/24/researchers-use-neural-networks-to-turn-face-sketches-into-photos/

Sketch Me That Shoe (CVPR 2016)

• project page: http://www.eecs.qmul.ac.uk/~qian/Project_cvpr16.html
• paper: http://www.eecs.qmul.ac.uk/~qian/SketchMeThatShoe.pdf
• github: https://github.com/seuliufeng/DeepSBIR

Mastering Sketching: Adversarial Augmentation for Structured Prediction

• keywords: Sketch Simplification
• project page: http://hi.cs.waseda.ac.jp/~esimo/en/research/sketch_master/
• arxiv: https://arxiv.org/abs/1703.08966
• github: https://github.com/bobbens/sketch_simplification

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

• intro: Georgia Institute of Technology
• arxiv: https://arxiv.org/abs/1801.02753

Image Stylization

Automatic Portrait Segmentation for Image Stylization

• intro: The Chinese University of Hong Kong & Adobe Research
• project page(data+code): http://xiaoyongshen.me/webpage_portrait/index.html
• paper: http://www.cse.cuhk.edu.hk/leojia/papers/portrait_eg16.pdf
• github(Tensorflow): https://github.com/PetroWu/AutoPortraitMatting

Transfiguring Portraits

• paper: http://homes.cs.washington.edu/~kemelmi/Transfiguring_Portraits_Kemelmacher_SIGGRAPH2016.pdf

Stylize Aesthetic QR Code

• intro: Zhengzhou University & Zhejiang University
• arxiv: https://arxiv.org/abs/1803.01146

Image Colorization

Deep Colorization

• paper: http://www.cs.cityu.edu.hk/~qiyang/publications/iccv-15.pdf

Learning Large-Scale Automatic Image Colorization

• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Deshpande_Learning_Large-Scale_Automatic_ICCV_2015_paper.pdf

Learning Representations for Automatic Colorization

• homepage: http://people.cs.uchicago.edu/~larsson/colorization/
• arxiv: http://arxiv.org/abs/1603.06668
• github: https://github.com/gustavla/autocolorize

Colorful Image Colorization

• intro: ECCV 2016
• project page: http://richzhang.github.io/colorization/
• arxiv: http://arxiv.org/abs/1603.08511
• github: https://github.com/richzhang/colorization
• demo: http://demos.algorithmia.com/colorize-photos/
• github: https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful github(Tensorflow): https://github.com/nilboy/colorization-tf

Colorising Black & White Photos using Deep Learning

https://hackernoon.com/colorising-black-white-photos-using-deep-learning-4da22a05f531

---

Automatic Colorization (Tensorflow + VGG)

• blog: http://tinyclouds.org/colorize/

colornet: Neural Network to colorize grayscale images

https://github.com/pavelgonchar/colornet

Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification (SIGGRAPH 2016)

• homepage: http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/
• paper: http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/data/colorization_sig2016.pdf
• github(Torch7): https://github.com/satoshiiizuka/siggraph2016_colorization

Convolutional autoencoder to colorize greyscale images

• github: https://github.com/YerevaNN/neural-colorizer

Image-Color: A deep learning approach to colorizing images

• github: https://github.com/cameronfabbri/Colorful-Image-Colorization

Creating an artificial artist: Color your photos using Neural Networks

• blog: https://www.analyticsvidhya.com/blog/2016/11/creating-an-artificial-artist-color-your-photos-using-neural-networks/

Paints Chainer: line drawing colorization using chainer

• github: https://github.com/pfnet/PaintsChainer
• demo: http://paintschainer.preferred.tech/

Unsupervised Diverse Colorization via Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1702.06674

(DE)^2 CO: Deep Depth Colorization

https://arxiv.org/abs/1703.10881

A Neural Representation of Sketch Drawings

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1704.03477

Real-Time User-Guided Image Colorization with Learned Deep Priors

• intro: SIGGRAPH 2017
• project page: https://richzhang.github.io/ideepcolor/
• arxiv: https://arxiv.org/abs/1705.02999
• github(official, Caffe): https://github.com/junyanz/interactive-deep-colorization

PixColor: Pixel Recursive Colorization

• intro: Google Research
• arxiv: https://arxiv.org/abs/1705.07208

cGAN-based Manga Colorization Using a Single Training Image

• intro: University of Tokyo
• arxiv: https://arxiv.org/abs/1706.06918

Interactive Deep Colorization With Simultaneous Global and Local Inputs

https://arxiv.org/abs/1801.09083

Image Colorization with Generative Adversarial Networks

https://arxiv.org/abs/1803.05400

Learning to Color from Language

• intro: Allen Institute of Artificial Intelligence & University of Massachusetts
• arxiv: https://arxiv.org/abs/1804.06026

Sounds

Visually Indicated Sounds

• project page: http://vis.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.08512

Music

GRUV: Algorithmic Music Generation using Recurrent Neural Networks

• github: https://github.com/MattVitelli/GRUV

DeepHear - Composing and harmonizing music with neural networks

• website: http://web.mit.edu/felixsun/www/neural-music.html
• github: https://github.com/fephsun/neuralnetmusic

Using AutoHarp and a Character-Based RNN to Create MIDI Drum Loops

• website: http://www.inquisitivists.com/2015/09/16/using-autoharp-and-a-character-based-rnn-to-create-midi-drum-loops

Musical Audio Synthesis Using Autoencoding Neural Nets

• paper: http://www.cs.dartmouth.edu/~sarroff/papers/sarroff2014a.pdf
• github: https://github.com/woodshop/deepAutoController/tree/icmc_smc_2014
• video: https://vimeo.com/121827215

sound-rnn: Generating sound using recurrent neural networks

• github: https://github.com/johnglover/sound-rnn
• blog: http://www.johnglover.net/blog/generating-sound-with-rnns.html

Using LSTM Recurrent Neural Networks for Music Generation (Project for AI Prac Fall 2015 at Cornell)

• youtube: https://www.youtube.com/watch?v=aSr8_QQYpYM
• video: http://video.weibo.com/show?fid=1034:4be01d679bb1a68a634fe0f589caa779

Visually Indicated Sounds (MIT. 2015)

• arxiv: http://arxiv.org/abs/1512.08512

Training a Recurrent Neural Network to Compose Music

• blog: https://maraoz.com/2016/02/02/abc-rnn/

LSTM Realbook

• blog: https://keunwoochoi.wordpress.com/2016/02/19/lstm-realbook/
• github: https://github.com/keunwoochoi/lstm_real_book

LSTMetallica: Generation drum tracks by learning the drum tracks of 60 Metallica songs

• blog: https://keunwoochoi.wordpress.com/2016/02/23/lstmetallica/

deepjazz: Deep learning driven jazz generation using Keras & Theano!

• homepage: https://jisungk.github.io/deepjazz/
• github：https://github.com/jisungk/deepjazz

Magenta: Music and Art Generation with Machine Intelligence

• homepage: http://magenta.tensorflow.org/
• github: https://github.com/tensorflow/magenta

Music Transcription with Convolutional Neural Networks

• blog: https://www.lunaverus.com/cnn
• download: https://www.lunaverus.com/download

Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics

• paper: http://cslab1.bc.edu/~csacademics/pdf/16Mikami.pdf
• github: https://github.com/mikamia

BachBot: Use deep learning to generate and harmonize music in the style of Bach

• intro: BachBot is a research project utilizing long short term memory (LSTMs) to generate Bach compositions
• homepage: http://bachbot.com/
• github: https://github.com/feynmanliang/bachbot

Generate Music in TensorFlow

• youtube: https://www.youtube.com/watch?v=ZE7qWXX05T0
• github: https://github.com/llSourcell/Music_Generator_Demo

Generate new lyrics in the style of any artist using LSTMs and TensorFlow

• github: https://github.com/dyelax/encore.ai

sound-GAN: Generative Adversial Network for music composition

• github: https://github.com/jacotar/sound-GAN

Analyzing Six Deep Learning Tools for Music Generation

• intro: Magenta / DeepJazz / BachBot / FlowMachines / WaveNet / GRUV
• blog: http://www.asimovinstitute.org/notes-vs-waves/

WIMP2: Creating Music with AI: Highlights of Current Research

• youtube: https://www.youtube.com/watch?v=5K4hn6cBUPU

Song From PI: A Musically Plausible Network for Pop Music Generation

• paper: http://openreview.net/pdf?id=ByBwSPcex
• project page: http://www.cs.toronto.edu/songfrompi/

Grammar Argumented LSTM Neural Networks with Note-Level Encoding for Music Composition

• arxiv: https://arxiv.org/abs/1611.05416

用TensorFlow生成周杰伦歌词

• blog: http://leix.me/2016/11/28/tensorflow-lyrics-generation/
• github: https://github.com/leido/char-rnn-cn

Hip-Hop - Generating lyrics with RNNs

• blog: http://affinelayer.com/hiphop/index.html

Metis Final Project: Music Composition with LSTMs

http://blog.naoya.io/metis-final-project-music-composition-with-lstms/

Neural Translation of Musical Style

• blog: http://imanmalik.com/cs/2017/06/05/neural-style.html
• github: https://github.com/imalikshake/StyleNet

Poetry

NeuralSnap: Generates poetry from images using convolutional and recurrent neural networks

• github: https://github.com/rossgoodwin/neuralsnap

Generating Chinese Classical Poems with RNN Encoder-Decoder

• arxiv: http://arxiv.org/abs/1604.01537

Chinese Poetry Generation with Planning based Neural Network

• intro: COLING 2016. University of Science and Technology of China & Baidu
• arxiv: https://arxiv.org/abs/1610.09889
• blog: http://freecoder.me/archives/213.html

Weiqi (Go)

Teaching Deep Convolutional Neural Networks to Play Go

• arxiv: http://arxiv.org/abs/1412.3409
• demo: https://chrisc36.github.io/deep-go/

Move Evaluation in Go Using Deep Convolutional Neural Networks(Google DeepMind, Google Brain)

• arxiv: http://arxiv.org/abs/1412.6564

Training Deep Convolutional Neural Networks to Play Go

• paper: http://jmlr.org/proceedings/papers/v37/clark15.pdf

Computer Go Research - The Challenges Ahead (Martin Müller. IEEE CIG 2015)

• homepage: https://webdocs.cs.ualberta.ca/~mmueller/talks.html

GoCNN: Using CNN for Go (Weiqi/Baduk) board evaluation with tensorflow

• github: https://github.com/jmgilmer/GoCNN

DarkGo: Go in Darknet

• homepage: http://pjreddie.com/darknet/darkgo-go-in-darknet/

BetaGo: Go bots for the people

• homepage: http://maxpumperla.github.io/betago/
• github: https://github.com/maxpumperla/betago

Deep Learning and the Game of Go

• book: https://www.manning.com/books/deep-learning-and-the-game-of-go
• github: https://github.com//maxpumperla/deep_learning_and_the_game_of_go

DarkForest

Better Computer Go Player with Neural Network and Long-term Prediction (Facebook AI Research)

• arxiv: http://arxiv.org/abs/1511.06410
• github: https://github.com/facebookresearch/darkforestGo
• MIT tech review: http://www.technologyreview.com/view/544181/how-facebooks-ai-researchers-built-a-game-changing-go-engine/

AlphaGo

Mastering the game of Go with deep neural networks and tree search

• intro: AlphaGo. Google DeepMind
• homepage: http://www.deepmind.com/alpha-go.html
• paper: https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf
• naturep page: http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html
• paper: https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
• slides: http://www.bioinfo.org.cn/~casp/temp/alphago_slides.pdf
• blog: http://www.furidamu.org/blog/2016/01/26/mastering-the-game-of-go-with-deep-neural-networks-and-tree-search/
• blog(“AlphaGo: From Intuitive Learning to Holistic Knowledge”): https://caminao.wordpress.com/2016/02/01/alphago/
• github: https://github.com/Rochester-NRT/AlphaGo

AlphaGo Teach

• intro: Let the AlphaGo Teaching Tool help you find new and creative ways of playing Go
• homepage: https://alphagoteach.deepmind.com/

AlphaGo的分析

• intro: by 田渊栋
• blog: http://zhuanlan.zhihu.com/yuandong/20607684

How Alphago Works

• slides: http://www.slideshare.net/ShaneSeungwhanMoon/how-alphago-works
• slides: http://pan.baidu.com/s/1qXwagGW

AlphaGo in Depth

• intro: by Mark Chang
• slides: http://www.slideshare.net/ckmarkohchang/alphago-in-depth?qid=283ab3bc-7d04-4e14-a205-b0b671ca4099
• mirror: https://pan.baidu.com/s/1i5JNeRj

Leela

• intro: Leela is a strong Go playing program combining advances in Go programming and further original research into a small, easy to use graphical interface.
• homepage: https://sjeng.org/leela.html

Mastering the game of Go without human knowledge

• nature page: http://www.nature.com/nature/journal/v550/n7676/full/nature24270.html
• paper: https://deepmind.com/documents/119/agz_unformatted_nature.pdf
• notes: https://blog.acolyer.org/2017/11/17/mastering-the-game-of-go-without-human-knowledge/

Computer Go & AlphaGo Zero

• youtube: https://www.youtube.com/watch?v=6fKG4wJ7uBk
• mirror: https://www.bilibili.com/video/av16428694/
• slides: https://drive.google.com/file/d/1rmUyIitEmAtMUKdKEnlHRfmXtpyoxxey/view

AlphaZero: Mastering Games without Human Knowledge - NIPS 2017

• intro: Keynote by David Silver on AlphaGo, AlphaGo Zero and AlphaZero, at the 2017 NIPS Deep Reinforcement Learning Symposium, 6 Dec, Long Beach, CA
• youtube: https://www.youtube.com/watch?v=A3ekFcZ3KNw
• mirror: https://www.bilibili.com/video/av17210816/

The future is here – AlphaZero learns chess

https://en.chessbase.com/post/the-future-is-here-alphazero-learns-chess

**AlphaGo Zero Cheat Sheet

https://applied-data.science/static/main/res/alpha_go_zero_cheat_sheet.png

Chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

• intro: MSc thesis
• arxiv: http://arxiv.org/abs/1509.01549

Spawkfish: neural network based chess engine

• homepage: http://spawk.fish/

Chess position evaluation with convolutional neural network in Julia

• blog: http://int8.io/chess-position-evaluation-with-convolutional-neural-networks-in-julia/

Deep Learning for … Chess

• blog: http://blog.yhat.com/posts/deep-learning-chess.html
• github: https://github.com/erikbern/deep-pink

DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

• intro: Winner of Best Paper Award in ICANN 2016
• arxiv: https://arxiv.org/abs/1711.09667
• paper: http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf
• github: https://github.com/mr-press/DeepChess

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1712.01815

Game

Learning Game of Life with a Convolutional Neural Network

• blog: http://danielrapp.github.io/cnn-gol/
• github: https://github.com/DanielRapp/cnn-gol

Reinforcement Learning using Tensor Flow: A deep Q learning demonstration using Google Tensorflow

• github: https://github.com/nivwusquorum/tensorflow-deepq

Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks

• arxiv: http://arxiv.org/abs/1509.06731
• paper: http://colinraffel.com/publications/aaai2016poker.pdf
• github: https://github.com/moscow25/deep_draw
• slides: https://drive.google.com/file/d/0B5eOIUHA0khiMjN1YnEtZHMwams/view
• slides: http://pan.baidu.com/s/1nu5zpZ7

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

• intro: Connecting Torch to StarCraft
• arxiv: https://arxiv.org/abs/1611.00625
• github: https://github.com/TorchCraft/TorchCraft

BlizzCon 2016 DeepMind and StarCraft II Deep Learning Panel Transcript

• part 1: http://starcraft.blizzplanet.com/blog/comments/blizzcon-2016-deepmind-and-starcraft-ii-deep-learning-panel-transcript
• part 2: http://starcraft.blizzplanet.com/blog/comments/blizzcon-2016-deepmind-and-starcraft-ii-deep-learning-panel-transcript/2

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

• arxiv: https://arxiv.org/abs/1701.01724
• github: https://github.com/lifrordi/DeepStack-Leduc

Gym StarCraft: StarCraft environment for OpenAI Gym, based on Facebook’s TorchCraft

• intro: Gym StarCraft is an environment bundle for OpenAI Gym. It is based on Facebook’s TorchCraft, which is a bridge between Torch and StarCraft for AI research.
• github: https://github.com/deepcraft/gym-starcraft

Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

https://arxiv.org/abs/1703.10069

Learning Macromanagement in StarCraft from Replays using Deep Learning

• intro: CIG 2017. IT University of Copenhagen
• arxiv: https://arxiv.org/abs/1707.03743

Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report

• intro: Czech Technical University in Prague
• arxiv: https://arxiv.org/abs/1801.02193
• gihtub: https://github.com/Games-and-Simulations/sc-docker

DeepLeague

DeepLeague: leveraging computer vision and deep learning on the League of Legends mini map + giving away a dataset of over 100,000 labeled images to further esports analytics research

• blog: https://medium.com/@farzatv/deepleague-leveraging-computer-vision-and-deep-learning-on-the-league-of-legends-mini-map-giving-d275fd17c4e0

DeepLeague (Part 2): The Technical Details

• blog: https://medium.com/@farzatv/deepleague-part-2-the-technical-details-374439e7e09a
• github: https://github.com/farzaa/DeepLeague

Courses

Learning Machines

http://www.patrickhebron.com/learning-machines/

Learning Bit by Bit

https://itp.nyu.edu/varwiki/Syllabus/LearningBitbyBitS10

MACHINE LEARNING FOR MUSICIANS AND ARTISTS (Course opens January 2016)

https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info

Machine learning for artists @ ITP-NYU, Spring 2016

• videos/lectures/course notes: http://ml4a.github.io/classes/itp-S16/
• index: http://ml4a.github.io/index/
• github: https://github.com/ml4a/ml4a.github.io
• notes: http://www.kdnuggets.com/2016/04/machine-learning-artists-video-lectures-notes.html
• blog: https://medium.com/@genekogan/machine-learning-for-artists-e93d20fdb097#.25w95beqb

Machine Learning for Artists @ OpenDot, November 2016

• homepage: http://ml4a.github.io/classes/opendot/

The Neural Aesthetic @ SchoolOfMa, Summer 2016

http://ml4a.github.io/classes/neural-aesthetic/

Blogs

Review of machine / deep learning in an artistic context

https://medium.com/@memoakten/machine-deep-learning-in-an-artistic-context-441f28774bcc#.gegpq99ag

Apprentice Work

https://www.technologyreview.com/s/600762/apprentice-work/

Exploring the Intersection of Art and Machine Intelligence

http://googleresearch.blogspot.jp/2016/02/exploring-intersection-of-art-and.html

Using machine learning to generate music

http://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-generate-music

art in the age of machine intelligence

https://medium.com/artists-and-machine-intelligence/what-is-ami-ccd936394a83#.hyt4ei9a9

Understanding Aesthetics with Deep Learning

https://devblogs.nvidia.com/parallelforall/understanding-aesthetics-deep-learning/

Go, Marvin Minsky, and the Chasm that AI Hasn’t Yet Crossed

blog: https://medium.com/backchannel/has-deepmind-really-passed-go-adc85e256bec#.inx8nfid0

A Return to Machine Learning

• intro: This post is aimed at artists and other creative people who are interested in a survey of recent developments in machine learning research that intersect with art and culture.
• blog: https://medium.com/@kcimc/a-return-to-machine-learning-2de3728558eb#.bp2b1ax2x

Resources

Music, Art and Machine Intelligence Workshop 2016

• youtube: https://www.youtube.com/playlist?list=PL8h7Hk91ScJ2NKFS_i8y4DYk4Zg1LHn0a