Object Detection

Method backbone test size VOC2007 VOC2010 VOC2012 ILSVRC 2013 MSCOCO 2015 Speed
OverFeat           24.3%    
R-CNN AlexNet   58.5% 53.7% 53.3% 31.4%    
R-CNN VGG16   66.0%          
SPP_net ZF-5   54.2%     31.84%    
DeepID-Net     64.1%     50.3%    
NoC 73.3%   68.8%          
Fast-RCNN VGG16   70.0% 68.8% 68.4%   19.7%(@[0.5-0.95]), 35.9%(@0.5)  
MR-CNN 78.2%   73.9%          
Faster-RCNN VGG16   78.8%   75.9%   21.9%(@[0.5-0.95]), 42.7%(@0.5) 198ms
Faster-RCNN ResNet101   85.6%   83.8%   37.4%(@[0.5-0.95]), 59.0%(@0.5)  
YOLO     63.4%   57.9%     45 fps
YOLO VGG-16     66.4%         21 fps
YOLOv2   448x448 78.6%   73.4%   21.6%(@[0.5-0.95]), 44.0%(@0.5) 40 fps
SSD VGG16 300x300 77.2%   75.8%   25.1%(@[0.5-0.95]), 43.1%(@0.5) 46 fps
SSD VGG16 512x512 79.8%   78.5%   28.8%(@[0.5-0.95]), 48.5%(@0.5) 19 fps
SSD ResNet101 300x300         28.0%(@[0.5-0.95]) 16 fps
SSD ResNet101 512x512         31.2%(@[0.5-0.95]) 8 fps
DSSD ResNet101 300x300         28.0%(@[0.5-0.95]) 8 fps
DSSD ResNet101 500x500         33.2%(@[0.5-0.95]) 6 fps
ION     79.2%   76.4%      
CRAFT     75.7%   71.3% 48.5%    
OHEM     78.9%   76.3%   25.5%(@[0.5-0.95]), 45.9%(@0.5)  
R-FCN ResNet50   77.4%         0.12sec(K40), 0.09sec(TitianX)
R-FCN ResNet101   79.5%         0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train) ResNet101   83.6%   82.0%   31.5%(@[0.5-0.95]), 53.2%(@0.5)  
PVANet 9.0     84.9%   84.2%     750ms(CPU), 46ms(TitianX)
RetinaNet ResNet101-FPN              
Light-Head R-CNN Xception* 800/1200         31.5%@[0.5:0.95] 95 fps
Light-Head R-CNN Xception* 700/1100         30.7%@[0.5:0.95] 102 fps

Published: 09 Oct 2015

Object Counting

Object Counting

Published: 09 Oct 2015

Natural Language Processing

Tutorials

Practical Neural Networks for NLP

Structured Neural Networks for NLP: From Idea to Code

Understanding Deep Learning Models in NLP

http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

Deep learning for natural language processing, Part 1

https://softwaremill.com/deep-learning-for-nlp/

Neural Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Visualizing and Understanding Neural Models in NLP

Character-Aware Neural Language Models

Skip-Thought Vectors

A Primer on Neural Network Models for Natural Language Processing

Character-aware Neural Language Models

Neural Variational Inference for Text Processing

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

MUSIO: A Deep Learning based Chatbot Getting Smarter

Translation

Learning phrase representations using rnn encoder-decoder for statistical machine translation

Neural Machine Translation by Jointly Learning to Align and Translate

Multi-Source Neural Translation

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

Modeling Coverage for Neural Machine Translation

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

NEMATUS: Attention-based encoder-decoder model for neural machine translation

Variational Neural Machine Translation

Neural Network Translation Models for Grammatical Error Correction

Linguistic Input Features Improve Neural Machine Translation

Sequence-Level Knowledge Distillation

Neural Machine Translation: Breaking the Performance Plateau

Tips on Building Neural Machine Translation Systems

Semi-Supervised Learning for Neural Machine Translation

EUREKA-MangoNMT: A C++ toolkit for neural machine translation for CPU

Deep Character-Level Neural Machine Translation

Neural Machine Translation Implementations

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Learning to Translate in Real-time with Neural Machine Translation

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

Fully Character-Level Neural Machine Translation without Explicit Segmentation

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Neural Machine Translation in Linear Time

Neural Machine Translation with Reconstruction

A Convolutional Encoder Model for Neural Machine Translation

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

MXNMT: MXNet based Neural Machine Translation

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

Massive Exploration of Neural Machine Translation Architectures

Depthwise Separable Convolutions for Neural Machine Translation

Deep Architectures for Neural Machine Translation

Marian: Fast Neural Machine Translation in C++

Sockeye

Summarization

Extraction of Salient Sentences from Labelled Documents

A Neural Attention Model for Abstractive Sentence Summarization

A Convolutional Attention Network for Extreme Summarization of Source Code

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

textsum: Text summarization with TensorFlow

How to Run Text Summarization with TensorFlow

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Separating Answers from Queries for Neural Reading Comprehension

Attention-over-Attention Neural Networks for Reading Comprehension

Teaching Machines to Read and Comprehend CNN News and Children Books using Torch

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

NewsQA: A Machine Comprehension Dataset

Gated-Attention Readers for Text Comprehension

Get To The Point: Summarization with Pointer-Generator Networks

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

Neural Semantic Encoders

Neural Tree Indexers for Text Understanding

Better Text Understanding Through Image-To-Text Transfer

Text Classification

Convolutional Neural Networks for Sentence Classification

Recurrent Convolutional Neural Networks for Text Classification

Character-level Convolutional Networks for Text Classification

A C-LSTM Neural Network for Text Classification

Rationale-Augmented Convolutional Neural Networks for Text Classification

Text classification using DIGITS and Torch7

Recurrent Neural Network for Text Classification with Multi-Task Learning

Deep Multi-Task Learning with Shared Memory

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

Sentence Convolution Code in Torch: Text classification using a convolutional neural network

Bag of Tricks for Efficient Text Classification

Actionable and Political Text Classification using Word Embeddings and LSTM

Implementing a CNN for Text Classification in TensorFlow

fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

Tweet Classification using RNN and CNN

Hierarchical Attention Networks for Document Classification

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

Generative and Discriminative Text Classification with Recurrent Neural Networks

Adversarial Multi-task Learning for Text Classification

Deep Text Classification Can be Fooled

Deep neural network framework for multi-label text classification

Multi-Task Label Embedding for Text Classification

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

Alignment

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Dialog

Visual Dialog

Papers, code and data from FAIR for various memory-augmented nets with application to text understanding and dialogue.

Neural Emoji Recommendation in Dialogue Systems

Memory Networks

Neural Turing Machines

Memory Networks

End-To-End Memory Networks

Reinforcement Learning Neural Turing Machines - Revised


Learning to Transduce with Unbounded Memory

How to Code and Understand DeepMind’s Neural Stack Machine


Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

Structured Memory for Neural Turing Machines

Dynamic Memory Networks for Visual and Textual Question Answering

Neural GPUs Learn Algorithms

Hierarchical Memory Networks

Convolutional Residual Memory Networks

NTM-Lasagne: A Library for Neural Turing Machines in Lasagne

Evolving Neural Turing Machines for Reward-based Learning

Hierarchical Memory Networks for Answer Selection on Unknown Words

Gated End-to-End Memory Networks

Can Active Memory Replace Attention?

A Taxonomy for Neural Memory Networks

Papers

Globally Normalized Transition-Based Neural Networks

A Decomposable Attention Model for Natural Language Inference

Improving Recurrent Neural Networks For Sequence Labelling

Recurrent Memory Networks for Language Modeling

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

Learning text representation using recurrent convolutional neural network with highway layers

Ask the GRU: Multi-task Learning for Deep Text Recommendations

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

Visualizing Linguistic Shift

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

Recent Trends in Deep Learning Based Natural Language Processing

HotFlip: White-Box Adversarial Examples for NLP

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

Interesting Applications

Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

sk_p: a neural program corrector for MOOCs

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

emoji2vec: Learning Emoji Representations from their Description

Inside-Outside and Forward-Backward Algorithms Are Just Backprop (Tutorial Paper)

Cruciform: Solving Crosswords with Natural Language Processing

Smart Reply: Automated Response Suggestion for Email

Deep Learning for RegEx

Learning Python Code Suggestion with a Sparse Pointer Network

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

https://arxiv.org/abs/1703.02458

Convolutional Sequence to Sequence Learning

DeepFix: Fixing Common C Language Errors by Deep Learning

Hierarchically-Attentive RNN for Album Summarization and Storytelling

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

NLP-Caffe: natural language processing with Caffe

DL4NLP: Deep Learning for Natural Language Processing

Combining CNN and RNN for spoken language identification

Character-Aware Neural Language Models: LSTM language model with CNN over characters in TensorFlow

Neural Relation Extraction with Selective Attention over Instances

deep-simplification: Text simplification using RNNs

lamtram: A toolkit for language and translation modeling using neural networks

Lango: Language Lego

Sequence-to-Sequence Learning with Attentional Neural Networks

harvardnlp code

Seq2seq: Sequence to Sequence Learning with Keras

debug seq2seq

Recurrent & convolutional neural network modules

Datasets

Datasets for Natural Language Processing

Blogs

How to read: Character level deep learning

Heavy Metal and Natural Language Processing

Sequence To Sequence Attention Models In PyCNN

https://talbaumel.github.io/Neural+Attention+Mechanism.html

Source Code Classification Using Deep Learning

http://blog.aylien.com/source-code-classification-using-deep-learning/

My Process for Learning Natural Language Processing with Deep Learning

https://medium.com/@MichaelTeifel/my-process-for-learning-natural-language-processing-with-deep-learning-bd0a64a36086

Convolutional Methods for Text

https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

Word2Vec

Word2Vec Tutorial - The Skip-Gram Model

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

Word2Vec Tutorial Part 2 - Negative Sampling

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

Word2Vec Resources

http://mccormickml.com/2016/04/27/word2vec-resources/

Demos

AskImage.org - Deep Learning for Answering Questions about Images

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

Resources

So, you need to understand language data? Open-source NLP software can help!

Curated list of resources on building bots

Notes for deep learning on NLP

https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7

Published: 09 Oct 2015

Neural Architecture Search

Papers

Published: 09 Oct 2015

Acceleration and Model Compression

Papers

Published: 09 Oct 2015

Graph Convolutional Networks

Learning Convolutional Neural Networks for Graphs

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Semi-Supervised Classification with Graph Convolutional Networks

Graph Based Convolutional Neural Network

How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)

http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/

Graph Convolutional Networks

DeepGraph: Graph Structure Predicts Network Growth

Deep Learning with Sets and Point Clouds

Deep Learning on Graphs

Robust Spatial Filtering with Graph Convolutional Neural Networks

https://arxiv.org/abs/1703.00792

Modeling Relational Data with Graph Convolutional Networks

https://arxiv.org/abs/1703.06103

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Deep Learning on Graphs with Graph Convolutional Networks

Deep Learning on Graphs with Keras

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

https://arxiv.org/abs/1708.04675

Graph Attention Networks

Residual Gated Graph ConvNets

https://arxiv.org/abs/1711.07553

Probabilistic and Regularized Graph Convolutional Networks

Videos as Space-Time Region Graphs

https://arxiv.org/abs/1806.01810

Relational inductive biases, deep learning, and graph networks

Can GCNs Go as Deep as CNNs?

GMNN: Graph Markov Neural Networks

Published: 09 Oct 2015

Generative Adversarial Networks

Generative Adversarial Networks

Generative Adversarial Nets

Adversarial Feature Learning

Generative Adversarial Networks

Adversarial Examples and Adversarial Training

How to Train a GAN? Tips and tricks to make GANs work

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Learning Interpretable Latent Representations with InfoGAN: A tutorial on implementing InfoGAN in Tensorflow

Coupled Generative Adversarial Networks

Energy-based Generative Adversarial Network

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Connecting Generative Adversarial Networks and Actor-Critic Methods

Generative Adversarial Nets from a Density Ratio Estimation Perspective

Unrolled Generative Adversarial Networks

Generative Adversarial Networks as Variational Training of Energy Based Models

Multi-class Generative Adversarial Networks with the L2 Loss Function

Least Squares Generative Adversarial Networks

Inverting The Generator Of A Generative Adversarial Networ

ml4a-invisible-cities

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Associative Adversarial Networks

Temporal Generative Adversarial Nets

Handwriting Profiling using Generative Adversarial Networks

  • intro: Accepted at The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17 Student Abstract and Poster Program)
  • arxiv: https://arxiv.org/abs/1611.08789

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Ensembles of Generative Adversarial Networks

Improved generator objectives for GANs

Stacked Generative Adversarial Networks

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

AdaGAN: Boosting Generative Models

Towards Principled Methods for Training Generative Adversarial Networks

Wasserstein GAN

Improved Training of Wasserstein GANs

On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

Controllable Generative Adversarial Network

Generative Adversarial Networks: An Overview

  • intro: Imperial College London & Victoria University of Wellington & University of Montreal & Cortexica Vision Systems Ltd
  • intro: IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding
  • arxiv: https://arxiv.org/abs/1710.07035

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

https://arxiv.org/abs/1711.03213

Spectral Normalization for Generative Adversarial Networks

https://openreview.net/forum?id=B1QRgziT-

Are GANs Created Equal? A Large-Scale Study

GAGAN: Geometry-Aware Generative Adverserial Networks

https://arxiv.org/abs/1712.00684

CycleGAN: a Master of Steganography

PacGAN: The power of two samples in generative adversarial networks

ComboGAN: Unrestrained Scalability for Image Domain Translation

Decoupled Learning for Conditional Adversarial Networks

https://arxiv.org/abs/1801.06790

No Modes left behind: Capturing the data distribution effectively using GANs

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

On GANs and GMMs

https://arxiv.org/abs/1805.12462

The Unusual Effectiveness of Averaging in GAN Training

https://arxiv.org/abs/1806.04498

Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis

https://arxiv.org/abs/1807.00751

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Which Training Methods for GANs do actually Converge?

Convergence Problems with Generative Adversarial Networks (GANs)

Bayesian CycleGAN via Marginalizing Latent Sampling

https://arxiv.org/abs/1811.07465

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

https://arxiv.org/abs/1811.10597

Do GAN Loss Functions Really Matter?

https://arxiv.org/abs/1811.09567

Image-to-Image Translation

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

Remastering Classic Films in Tensorflow with Pix2Pix

Image-to-Image Translation in Tensorflow

webcam pix2pix

https://github.com/memo/webcam-pix2pix-tensorflow


Unsupervised Image-to-Image Translation with Generative Adversarial Networks

Unsupervised Image-to-Image Translation Networks

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

CycleGAN and pix2pix in PyTorch

Perceptual Adversarial Networks for Image-to-Image Transformation

https://arxiv.org/abs/1706.09138

XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

https://arxiv.org/abs/1711.09334

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

https://arxiv.org/abs/1711.09554

Toward Multimodal Image-to-Image Translation

Face Translation between Images and Videos using Identity-aware CycleGAN

https://arxiv.org/abs/1712.00971

Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders

https://arxiv.org/abs/1712.02050

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

On the Effectiveness of Least Squares Generative Adversarial Networks

https://arxiv.org/abs/1712.06391

GANs for Limited Labeled Data

Defending Against Adversarial Examples

Conditional Image-to-Image Translation

XOGAN: One-to-Many Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.07277

Unsupervised Attention-guided Image to Image Translation

https://arxiv.org/abs/1806.02311

Exemplar Guided Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.11145

Improving Shape Deformation in Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1808.04325

Video-to-Video Synthesis

Segmentation Guided Image-to-Image Translation with Adversarial Networks

https://arxiv.org/abs/1901.01569

Projects

Generative Adversarial Networks with Keras

Generative Adversarial Network Demo for Fresh Machine Learning #2

TextGAN: A generative adversarial network for text generation, written in TensorFlow.

cleverhans v0.1: an adversarial machine learning library

Deep Convolutional Variational Autoencoder w/ Adversarial Network

A versatile GAN(generative adversarial network) implementation. Focused on scalability and ease-of-use.

AdaGAN: Boosting Generative Models

TensorFlow-GAN (TFGAN)

Blogs

Generative Adversial Networks Explained

Generative Adversarial Autoencoders in Theano

An introduction to Generative Adversarial Networks (with code in TensorFlow)

Difficulties training a Generative Adversarial Network

Are Energy-Based GANs any more energy-based than normal GANs?

http://www.inference.vc/are-energy-based-gans-actually-energy-based/

Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode: Plus a Tensorflow tutorial for implementing your own GAN

Deep Learning Research Review Week 1: Generative Adversarial Nets

Stability of Generative Adversarial Networks

Instance Noise: A trick for stabilising GAN training

Generating Fine Art in 300 Lines of Code

Talks / Videos

Generative Adversarial Network visualization

Resources

The GAN Zoo

AdversarialNetsPapers: The classical Papers about adversial nets

GAN Timeline

Published: 09 Oct 2015

Fun With Deep Learning

Painting

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

Emoji

Brewing EmojiNet

Image2Emoji: Zero-shot Emoji Prediction for Visual Media

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

Text input with relevant emoji sorted with deeplearning

Sketch

Sketch-a-Net that Beats Humans

How Do Humans Sketch Objects?

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

Convolutional Sketch Inversion

Sketch Me That Shoe (CVPR 2016)

Mastering Sketching: Adversarial Augmentation for Structured Prediction

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Image Colorization

Deep Colorization

Learning Large-Scale Automatic Image Colorization

Learning Representations for Automatic Colorization

Colorful Image Colorization

Colorising Black & White Photos using Deep Learning

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


Automatic Colorization (Tensorflow + VGG)

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)

Convolutional autoencoder to colorize greyscale images

Image-Color: A deep learning approach to colorizing images

Creating an artificial artist: Color your photos using Neural Networks

Paints Chainer: line drawing colorization using chainer

Unsupervised Diverse Colorization via Generative Adversarial Networks

(DE)^2 CO: Deep Depth Colorization

https://arxiv.org/abs/1703.10881

A Neural Representation of Sketch Drawings

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

PixColor: Pixel Recursive Colorization

cGAN-based Manga Colorization Using a Single Training Image

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

Deep Exemplar-based Colorization

Pixel-level Semantics Guided Image Colorization

https://arxiv.org/abs/1808.01597

User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks

Pixelated Semantic Colorization

https://arxiv.org/abs/1901.10889

Sounds

Visually Indicated Sounds

Music

GRUV: Algorithmic Music Generation using Recurrent Neural Networks

DeepHear - Composing and harmonizing music with neural networks

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

Musical Audio Synthesis Using Autoencoding Neural Nets

sound-rnn: Generating sound using recurrent neural networks

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

Visually Indicated Sounds (MIT. 2015)

Training a Recurrent Neural Network to Compose Music

LSTM Realbook

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

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

Magenta: Music and Art Generation with Machine Intelligence

Music Transcription with Convolutional Neural Networks

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

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

Generate Music in TensorFlow

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

sound-GAN: Generative Adversial Network for music composition

Analyzing Six Deep Learning Tools for Music Generation

WIMP2: Creating Music with AI: Highlights of Current Research

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

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

用TensorFlow生成周杰伦歌词

Hip-Hop - Generating lyrics with RNNs

Metis Final Project: Music Composition with LSTMs

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

Neural Translation of Musical Style

Poetry

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

Generating Chinese Classical Poems with RNN Encoder-Decoder

Chinese Poetry Generation with Planning based Neural Network

Weiqi (Go)

Teaching Deep Convolutional Neural Networks to Play Go

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

Training Deep Convolutional Neural Networks to Play Go

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

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

DarkGo: Go in Darknet

BetaGo: Go bots for the people

Deep Learning and the Game of Go

DarkForest

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

AlphaGo

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

AlphaGo Teach

AlphaGo的分析

How Alphago Works

AlphaGo in Depth

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

Computer Go & AlphaGo Zero

AlphaZero: Mastering Games without Human Knowledge - NIPS 2017

PhoenixGo

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

Spawkfish: neural network based chess engine

Chess position evaluation with convolutional neural network in Julia

Deep Learning for … Chess

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

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

Game

Learning Game of Life with a Convolutional Neural Network

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

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

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

BlizzCon 2016 DeepMind and StarCraft II Deep Learning Panel Transcript

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

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

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

Macro action selection with deep reinforcement learning in StarCraft

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

DeepLeague (Part 2): The Technical Details

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

Machine Learning for Artists @ OpenDot, November 2016

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

Resources

Music, Art and Machine Intelligence Workshop 2016

Published: 09 Oct 2015