Object Counting

Tutorials

Practical Neural Networks for NLP

• intro: EMNLP 2016
• github: https://github.com/clab/dynet_tutorial_examples

Structured Neural Networks for NLP: From Idea to Code

• slides: https://github.com/neubig/yrsnlp-2016/blob/master/neubig16yrsnlp.pdf
• github: https://github.com/neubig/yrsnlp-2016

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

• intro: NIPS 2014 deep learning workshop
• arxiv: http://arxiv.org/abs/1411.2539
• github: https://github.com/ryankiros/visual-semantic-embedding
• results: http://www.cs.toronto.edu/~rkiros/lstm_scnlm.html
• demo: http://deeplearning.cs.toronto.edu/i2t

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

• arxiv: http://arxiv.org/abs/1503.00075
• github: https://github.com/stanfordnlp/treelstm
• github(Theano): https://github.com/ofirnachum/tree_rnn

Visualizing and Understanding Neural Models in NLP

• arxiv: http://arxiv.org/abs/1506.01066
• github: https://github.com/jiweil/Visualizing-and-Understanding-Neural-Models-in-NLP

Character-Aware Neural Language Models

• paper: http://arxiv.org/abs/1508.06615
• github: https://github.com/yoonkim/lstm-char-cnn

Skip-Thought Vectors

• paper: http://arxiv.org/abs/1506.06726
• github: https://github.com/ryankiros/skip-thoughts

A Primer on Neural Network Models for Natural Language Processing

• arxiv: http://arxiv.org/abs/1510.00726

Character-aware Neural Language Models

• arxiv: http://arxiv.org/abs/1508.06615

Neural Variational Inference for Text Processing

• arxiv: http://arxiv.org/abs/1511.06038
• notes: http://dustintran.com/blog/neural-variational-inference-for-text-processing/
• github: https://github.com/carpedm20/variational-text-tensorflow
• github: https://github.com/cheng6076/NVDM

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1510.09202

MUSIO: A Deep Learning based Chatbot Getting Smarter

• homepage: http://ec2-204-236-149-143.us-west-1.compute.amazonaws.com:9000/
• github(Torch7): https://github.com/deepcoord/seq2seq

Translation

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

• intro: GRU. EMNLP 2014
• arxiv: http://arxiv.org/abs/1406.1078

Neural Machine Translation by Jointly Learning to Align and Translate

• intro: ICLR 2015
• arxiv: http://arxiv.org/abs/1409.0473
• github: https://github.com/lisa-groundhog/GroundHog

Multi-Source Neural Translation

• intro: “report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.”
• arxiv: Multi-Source Neural Translation
• github(Zoph_RNN): https://github.com/isi-nlp/Zoph_RNN
• video: http://research.microsoft.com/apps/video/default.aspx?id=260336

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

• arxiv: http://arxiv.org/abs/1601.01073
• github: https://github.com/nyu-dl/dl4mt-multi
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/multi-way-nmt-shared-attention.md

Modeling Coverage for Neural Machine Translation

• arxiv: http://arxiv.org/abs/1601.04811

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

• arxiv: http://arxiv.org/abs/1603.06147
• github: https://github.com/nyu-dl/dl4mt-cdec

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

• github: https://github.com/rsennrich/nematus

Variational Neural Machine Translation

• intro: EMNLP 2016
• arxiv: https://arxiv.org/abs/1605.07869
• github: https://github.com/DeepLearnXMU/VNMT

Neural Network Translation Models for Grammatical Error Correction

• arxiv: http://arxiv.org/abs/1606.00189

Linguistic Input Features Improve Neural Machine Translation

• arxiv: http://arxiv.org/abs/1606.02892
• github: https://github.com/rsennrich/nematus

Sequence-Level Knowledge Distillation

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1606.07947
• github: https://github.com/harvardnlp/nmt-android

Neural Machine Translation: Breaking the Performance Plateau

• slides: http://www.meta-net.eu/events/meta-forum-2016/slides/09_sennrich.pdf

Tips on Building Neural Machine Translation Systems

• github: https://github.com/neubig/nmt-tips

Semi-Supervised Learning for Neural Machine Translation

• intro: ACL 2016. Tsinghua University & Baidu Inc
• arxiv: http://arxiv.org/abs/1606.04596

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

• github: https://github.com/jiajunzhangnlp/EUREKA-MangoNMT

Deep Character-Level Neural Machine Translation

• github: https://github.com/SwordYork/DCNMT

Neural Machine Translation Implementations

• github: https://github.com/jonsafari/nmt-list

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

• arxiv: http://arxiv.org/abs/1609.08144v1

Learning to Translate in Real-time with Neural Machine Translation

• arxiv: https://arxiv.org/abs/1610.00388

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

• arxiv: https://arxiv.org/abs/1610.01108
• github: https://github.com/emjotde/amunmt

Fully Character-Level Neural Machine Translation without Explicit Segmentation

• arxiv: https://arxiv.org/abs/1610.03017
• github: https://github.com/nyu-dl/dl4mt-c2c

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

• arxiv: https://arxiv.org/abs/1610.03164

Neural Machine Translation in Linear Time

• intro: ByteNet
• arxiv: https://arxiv.org/abs/1610.10099
• github: https://github.com/paarthneekhara/byteNet-tensorflow
• github(Tensorflow): https://github.com/buriburisuri/ByteNet

Neural Machine Translation with Reconstruction

• arxiv: https://arxiv.org/abs/1611.01874

A Convolutional Encoder Model for Neural Machine Translation

• intro: ACL 2017. Facebook AI Research
• arxiv: https://arxiv.org/abs/1611.02344
• github: https://github.com//pravarmahajan/cnn-encoder-nmt

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

• arxiv: https://arxiv.org/abs/1611.04798

MXNMT: MXNet based Neural Machine Translation

• github: https://github.com/magic282/MXNMT

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

• intro: Dublin City University & Trinity College Dublin
• arxiv: https://arxiv.org/abs/1702.01287

Massive Exploration of Neural Machine Translation Architectures

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1703.03906
• github: https://github.com/google/seq2seq/

Depthwise Separable Convolutions for Neural Machine Translation

• intro: Google Brain & University of Toronto
• arxiv: https://arxiv.org/abs/1706.03059

Deep Architectures for Neural Machine Translation

• intro: WMT 2017 research track. University of Edinburgh & Charles University
• arxiv: https://arxiv.org/abs/1707.07631
• github: https://github.com/Avmb/deep-nmt-architectures

Marian: Fast Neural Machine Translation in C++

• intro: Microsoft & Adam Mickiewicz University in Poznan & University of Edinburgh
• homepage: https://marian-nmt.github.io/
• arxiv: https://arxiv.org/abs/1804.00344
• github: https://github.com/marian-nmt/marian

Sockeye

• intro: Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
• arxiv: https://github.com/awslabs/sockeye/

Summarization

Extraction of Salient Sentences from Labelled Documents

• arxiv: http://arxiv.org/abs/1412.6815
• github: https://github.com/mdenil/txtnets
• notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2014/06/model-visualizing-summarising-conv-net.md

A Neural Attention Model for Abstractive Sentence Summarization

• intro: EMNLP 2015. Facebook AI Research
• arxiv: http://arxiv.org/abs/1509.00685
• github: https://github.com/facebook/NAMAS
• github(TensorFlow): https://github.com/carpedm20/neural-summary-tensorflow

A Convolutional Attention Network for Extreme Summarization of Source Code

• homepage: http://groups.inf.ed.ac.uk/cup/codeattention/
• arxiv: http://arxiv.org/abs/1602.03001
• github: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/02/conv-attention-network-source-code-summarization.md

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

• intro: BM Watson & Université de Montréal
• arxiv: http://arxiv.org/abs/1602.06023

textsum: Text summarization with TensorFlow

• blog: https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
• github: https://github.com/tensorflow/models/tree/master/textsum

How to Run Text Summarization with TensorFlow

• blog: https://medium.com/@surmenok/how-to-run-text-summarization-with-tensorflow-d4472587602d#.mll1rqgjg
• github: https://github.com/surmenok/TextSum

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

• intro: ACL 2016
• arxiv: https://arxiv.org/abs/1603.01547
• github: https://github.com/rkadlec/asreader

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

• arxiv: http://arxiv.org/abs/1606.02858
• github: https://github.com/danqi/rc-cnn-dailymail

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.02250
• dataset(“HFL-RC”): http://hfl.iflytek.com/chinese-rc/

Separating Answers from Queries for Neural Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.03316
• github: https://github.com/dirkweissenborn/qa_network

Attention-over-Attention Neural Networks for Reading Comprehension

• arxiv: http://arxiv.org/abs/1607.04423
• github: https://github.com/OlavHN/attention-over-attention

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

• github: https://github.com/ganeshjawahar/torch-teacher

Reasoning with Memory Augmented Neural Networks for Language Comprehension

• arxiv: https://arxiv.org/abs/1610.06454

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

• project page: https://allenai.github.io/bi-att-flow/
• github: https://github.com/allenai/bi-att-flow

NewsQA: A Machine Comprehension Dataset

• arxiv: https://arxiv.org/abs/1611.09830
• dataset: http://datasets.maluuba.com/NewsQA
• github: https://github.com/Maluuba/newsqa

Gated-Attention Readers for Text Comprehension

• intro: CMU
• arxiv: https://arxiv.org/abs/1606.01549
• github: https://github.com/bdhingra/ga-reader

Get To The Point: Summarization with Pointer-Generator Networks

• intro: ACL 2017. Stanford University & Google Brain
• arxiv: https://arxiv.org/abs/1704.04368
• github: https://github.com/abisee/pointer-generator

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

• arxiv: http://arxiv.org/abs/1506.00195
• github: https://github.com/npow/RNN-EM

Neural Semantic Encoders

• intro: EACL 2017
• arxiv: https://arxiv.org/abs/1607.04315
• github(Keras): https://github.com/pdasigi/neural-semantic-encoders

Neural Tree Indexers for Text Understanding

• arxiv: https://arxiv.org/abs/1607.04492
• bitbucket: https://bitbucket.org/tsendeemts/nti/src

Better Text Understanding Through Image-To-Text Transfer

• intro: Google Brain & Technische Universität München
• arxiv: https://arxiv.org/abs/1705.08386

Text Classification

Convolutional Neural Networks for Sentence Classification

• intro: EMNLP 2014
• arxiv: http://arxiv.org/abs/1408.5882
• github(Theano): https://github.com/yoonkim/CNN_sentence
• github(Torch): https://github.com/harvardnlp/sent-conv-torch
• github(Keras): https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras
• github(Tensorflow): https://github.com/abhaikollara/CNN-Sentence-Classification

Recurrent Convolutional Neural Networks for Text Classification

• paper: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745/9552
• github: https://github.com/knok/rcnn-text-classification

Character-level Convolutional Networks for Text Classification

• intro: NIPS 2015. “Text Understanding from Scratch”
• arxiv: http://arxiv.org/abs/1509.01626
• github: https://github.com/zhangxiangxiao/Crepe
• datasets: http://goo.gl/JyCnZq
• github(TensorFlow): https://github.com/mhjabreel/CharCNN

A C-LSTM Neural Network for Text Classification

• arxiv: http://arxiv.org/abs/1511.08630

Rationale-Augmented Convolutional Neural Networks for Text Classification

• arxiv: http://arxiv.org/abs/1605.04469

Text classification using DIGITS and Torch7

• github: https://github.com/NVIDIA/DIGITS/tree/master/examples/text-classification

Recurrent Neural Network for Text Classification with Multi-Task Learning

• arxiv: http://arxiv.org/abs/1605.05101

Deep Multi-Task Learning with Shared Memory

• intro: EMNLP 2016
• arxiv: https://arxiv.org/abs/1609.07222

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

• arxiv: http://arxiv.org/abs/1605.07725
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md

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

• github: https://github.com/harvardnlp/sent-conv-torch

Bag of Tricks for Efficient Text Classification

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1607.01759
• github: https://github.com/kemaswill/fasttext_torch
• github: https://github.com/facebookresearch/fastText

Actionable and Political Text Classification using Word Embeddings and LSTM

• arxiv: http://arxiv.org/abs/1607.02501

Implementing a CNN for Text Classification in TensorFlow

• blog: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/

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

• github: https://github.com/textclf/fancy-cnn

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

• arxiv: http://arxiv.org/abs/1609.00718

Tweet Classification using RNN and CNN

• github: https://github.com/ganeshjawahar/tweet-classify

Hierarchical Attention Networks for Document Classification

• intro: CMU & MSR. NAACL 2016
• paper: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf
• github(TensorFlow): https://github.com/raviqqe/tensorflow-font2char2word2sent2doc
• github(TensorFlow): https://github.com/ematvey/deep-text-classifier

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

• arxiv: https://arxiv.org/abs/1611.01884
• github(MXNet): https://github.com/Ldpe2G/AC-BLSTM

Generative and Discriminative Text Classification with Recurrent Neural Networks

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1703.01898

Adversarial Multi-task Learning for Text Classification

• intro: ACL 2017
• arxiv: https://arxiv.org/abs/1704.05742
• data: http://nlp.fudan.edu.cn/data/

Deep Text Classification Can be Fooled

• intro: Renmin University of China
• arxiv: https://arxiv.org/abs/1704.08006

Deep neural network framework for multi-label text classification

• github: https://github.com/inspirehep/magpie

Multi-Task Label Embedding for Text Classification

• intro: Shanghai Jiao Tong University
• arxiv: https://arxiv.org/abs/1710.07210

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

• intro: Chinese Academy of Sciences. accepted for publication in Neural Networks
• arxiv: https://arxiv.org/abs/1701.00185
• github: https://github.com/jacoxu/STC2

Alignment

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

• arxiv: http://arxiv.org/abs/1506.06724
• github: https://github.com/ryankiros/neural-storyteller

Dialog

Visual Dialog

• webiste: http://visualdialog.org/
• arxiv: https://arxiv.org/abs/1611.08669
• github: https://github.com/batra-mlp-lab/visdial-amt-chat
• github(Torch): https://github.com/batra-mlp-lab/visdial
• github(PyTorch): https://github.com/Cloud-CV/visual-chatbot
• demo: http://visualchatbot.cloudcv.org/

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

• post: https://www.facebook.com/yann.lecun/posts/10154070851697143

Neural Emoji Recommendation in Dialogue Systems

• intro: Tsinghua University & Baidu
• arxiv: https://arxiv.org/abs/1612.04609

Memory Networks

Neural Turing Machines

• paper: http://arxiv.org/abs/1410.5401
• Chs: http://www.jianshu.com/p/94dabe29a43b
• github: https://github.com/shawntan/neural-turing-machines
• github: https://github.com/DoctorTeeth/diffmem
• github: https://github.com/carpedm20/NTM-tensorflow
• blog: https://blog.aidangomez.ca/2016/05/16/The-Neural-Turing-Machine/

Memory Networks

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1410.3916
• github: https://github.com/npow/MemNN

End-To-End Memory Networks

• intro: Facebook AI Research
• intro: Continuous version of memory extraction via softmax. “Weakly supervised memory networks”
• arxiv: http://arxiv.org/abs/1503.08895
• github: https://github.com/facebook/MemNN
• github: https://github.com/vinhkhuc/MemN2N-babi-python
• github: https://github.com/npow/MemN2N
• github: https://github.com/domluna/memn2n
• github(Tensorflow): https://github.com/abhaikollara/MemN2N-Tensorflow
• video: http://research.microsoft.com/apps/video/default.aspx?id=259920&r=1
• video: http://pan.baidu.com/s/1pKiGLzP

Reinforcement Learning Neural Turing Machines - Revised

• arxiv: http://arxiv.org/abs/1505.00521
• github: https://github.com/ilyasu123/rlntm

Learning to Transduce with Unbounded Memory

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1506.02516

How to Code and Understand DeepMind’s Neural Stack Machine

• blog: https://iamtrask.github.io/2016/02/25/deepminds-neural-stack-machine/
• video tutorial: http://pan.baidu.com/s/1qX0EGDe

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

• intro: Memory networks implemented via rnns and gated recurrent units (GRUs).
• arxiv: http://arxiv.org/abs/1506.07285
• blog(“Implementing Dynamic memory networks”): http://yerevann.github.io//2016/02/05/implementing-dynamic-memory-networks/
• github(Python): https://github.com/swstarlab/DynamicMemoryNetworks

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

• intro: extensions for the Dynamic Memory Network (DMN)
• arxiv: https://arxiv.org/abs/1703.03939
• github: https://github.com/rgsachin/DMTN

Structured Memory for Neural Turing Machines

• intro: IBM Watson
• arxiv: http://arxiv.org/abs/1510.03931

Dynamic Memory Networks for Visual and Textual Question Answering

• intro: MetaMind 2016
• arxiv: http://arxiv.org/abs/1603.01417
• slides: http://slides.com/smerity/dmn-for-tqa-and-vqa-nvidia-gtc#/
• github: https://github.com/therne/dmn-tensorflow
• github(Theano): https://github.com/ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus
• review: https://www.technologyreview.com/s/600958/the-memory-trick-making-computers-seem-smarter/
• github(Tensorflow): https://github.com/DeepRNN/visual_question_answering

Neural GPUs Learn Algorithms

• arxiv: http://arxiv.org/abs/1511.08228
• github: https://github.com/tensorflow/models/tree/master/neural_gpu
• github: https://github.com/ikostrikov/torch-neural-gpu
• github: https://github.com/tristandeleu/neural-gpu

Hierarchical Memory Networks

• arxiv: http://arxiv.org/abs/1605.07427

Convolutional Residual Memory Networks

• arxiv: http://arxiv.org/abs/1606.05262

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

• github: https://github.com/snipsco/ntm-lasagne
• blog: https://medium.com/snips-ai/ntm-lasagne-a-library-for-neural-turing-machines-in-lasagne-2cdce6837315#.96pnh1m6j

Evolving Neural Turing Machines for Reward-based Learning

• homepage: http://sebastianrisi.com/entm/
• paper: http://sebastianrisi.com/wp-content/uploads/greve_gecco16.pdf
• code: https://www.dropbox.com/s/t019mwabw5nsnxf/neuralturingmachines-master.zip?dl=0

Hierarchical Memory Networks for Answer Selection on Unknown Words

• intro: COLING 2016
• arxiv: https://arxiv.org/abs/1609.08843
• github: https://github.com/jacoxu/HMN4QA

Gated End-to-End Memory Networks

• arxiv: https://arxiv.org/abs/1610.04211

Can Active Memory Replace Attention?

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1610.08613

A Taxonomy for Neural Memory Networks

• intro: University of Florida
• arxiv: https://arxiv.org/abs/1805.00327

Papers

Globally Normalized Transition-Based Neural Networks

• intro: speech tagging, dependency parsing and sentence compression
• arxiv: http://arxiv.org/abs/1603.06042
• github(SyntaxNet): https://github.com/tensorflow/models/tree/master/syntaxnet

A Decomposable Attention Model for Natural Language Inference

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1606.01933
• github(Keras+spaCy): https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment

Improving Recurrent Neural Networks For Sequence Labelling

• arxiv: http://arxiv.org/abs/1606.02555

Recurrent Memory Networks for Language Modeling

• arixv: http://arxiv.org/abs/1601.01272
• github: https://github.com/ketranm/RMN

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

• intro: MIT Media Lab
• arixv: http://arxiv.org/abs/1607.07514

Learning text representation using recurrent convolutional neural network with highway layers

• intro: Neu-IR ‘16 SIGIR Workshop on Neural Information Retrieval
• arxiv: http://arxiv.org/abs/1606.06905
• github: https://github.com/wenying45/deep_learning_tutorial/tree/master/rcnn-hw

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

• arxiv: http://arxiv.org/abs/1609.02116

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

• intro: COLING 2016
• arxiv: https://arxiv.org/abs/1610.03342

Visualizing Linguistic Shift

• arxiv: https://arxiv.org/abs/1611.06478

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

• intro: The University of Tokyo & Salesforce Research
• arxiv: https://arxiv.org/abs/1611.01587

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

• intro: Google Brain & Google Research & University of Toronto
• intro: Just attention + positional encoding = state of the art
• arxiv: https://arxiv.org/abs/1706.03762
• github(Chainer): https://github.com/soskek/attention_is_all_you_need

Recent Trends in Deep Learning Based Natural Language Processing

• intro: Beijing Institute of Technology & National University of Singapore & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1708.02709

HotFlip: White-Box Adversarial Examples for NLP

• intro: University of Oregon & Nanjing University
• arxiv: https://arxiv.org/abs/1712.06751

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

• intro: ACL 2018
• arxiv: https://arxiv.org/abs/1804.09160

Interesting Applications

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

• arxiv: http://arxiv.org/abs/1606.05611

sk_p: a neural program corrector for MOOCs

• intro: MIT
• intro: Using seq2seq to fix buggy code submissions in MOOCs
• arxiv: http://arxiv.org/abs/1607.02902

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

• intro: EMNLP 2016
• intro: translating natural language queries into regular expressions which embody their meaning
• arxiv: http://arxiv.org/abs/1608.03000

emoji2vec: Learning Emoji Representations from their Description

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1609.08359

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

• paper: https://www.cs.jhu.edu/~jason/papers/eisner.spnlp16.pdf

Cruciform: Solving Crosswords with Natural Language Processing

• arxiv: https://arxiv.org/abs/1611.02360

Smart Reply: Automated Response Suggestion for Email

• intro: Google. KDD 2016
• arxiv: https://arxiv.org/abs/1606.04870
• notes: https://blog.acolyer.org/2016/11/24/smart-reply-automated-response-suggestion-for-email/

Deep Learning for RegEx

• intro: a winning submission of Extraction of product attribute values competition (CrowdAnalytix)
• blog: http://dlacombejr.github.io/2016/11/13/deep-learning-for-regex.html

Learning Python Code Suggestion with a Sparse Pointer Network

• intro: Learning to Auto-Complete using RNN Language Models
• intro: University College London
• arxiv: https://arxiv.org/abs/1611.08307
• github: https://github.com/uclmr/pycodesuggest

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

• arxiv: https://arxiv.org/abs/1705.03122
• paper: https://s3.amazonaws.com/fairseq/papers/convolutional-sequence-to-sequence-learning.pdf
• github: https://github.com/facebookresearch/fairseq

DeepFix: Fixing Common C Language Errors by Deep Learning

• intro: AAAI 2017. Indian Institute of Science
• project page: http://www.iisc-seal.net/deepfix
• paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14603/13921
• bitbucket: https://bitbucket.org/iiscseal/deepfix

Hierarchically-Attentive RNN for Album Summarization and Storytelling

• intro: EMNLP 2017. UNC Chapel Hill
• arxiv: https://arxiv.org/abs/1708.02977

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

• arxiv: http://arxiv.org/abs/1605.00942
• github: https://github.com/senarvi/theanolm

NLP-Caffe: natural language processing with Caffe

• github: https://github.com/Russell91/nlpcaffe

DL4NLP: Deep Learning for Natural Language Processing

• github: https://github.com/nokuno/dl4nlp

Combining CNN and RNN for spoken language identification

• blog: http://yerevann.github.io//2016/06/26/combining-cnn-and-rnn-for-spoken-language-identification/
• github: https://github.com/YerevaNN/Spoken-language-identification/tree/master/theano

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

• github: https://github.com/carpedm20/lstm-char-cnn-tensorflow

Neural Relation Extraction with Selective Attention over Instances

• paper: http://nlp.csai.tsinghua.edu.cn/~lzy/publications/acl2016_nre.pdf
• github: https://github.com/thunlp/NRE

deep-simplification: Text simplification using RNNs

• intro: achieves a BLEU score of 61.14
• github: https://github.com/mbartoli/deep-simplification

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

• github: https://github.com/neubig/lamtram

Lango: Language Lego

• intro: Lango is a natural language processing library for working with the building blocks of language.
• github: https://github.com/ayoungprogrammer/Lango

Sequence-to-Sequence Learning with Attentional Neural Networks

• github(Torch): https://github.com/harvardnlp/seq2seq-attn

harvardnlp code

• intro: pen-source implementations of popular deep learning techniques with applications to NLP
• homepage: http://nlp.seas.harvard.edu/code/

Seq2seq: Sequence to Sequence Learning with Keras

• github: https://github.com/farizrahman4u/seq2seq

debug seq2seq

• github: https://github.com/nicolas-ivanov/debug_seq2seq

Recurrent & convolutional neural network modules

• intro: This repo contains Theano implementations of popular neural network components and optimization methods.
• github: https://github.com/taolei87/rcnn

Datasets

Datasets for Natural Language Processing

• github: https://github.com/karthikncode/nlp-datasets

Blogs

How to read: Character level deep learning

• blog: https://offbit.github.io/how-to-read/
• github: https://github.com/offbit/char-models

Heavy Metal and Natural Language Processing

• part 1: http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/

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

• homepage: http://www.askimage.org/

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

• youtube: https://www.youtube.com/watch?v=7s-erJbCkaY

Resources

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

• blog: http://entopix.com/so-you-need-to-understand-language-data-open-source-nlp-software-can-help.html

Curated list of resources on building bots

• github: https://github.com/hackerkid/bots

Notes for deep learning on NLP

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

Papers

Papers

Papers

Learning Convolutional Neural Networks for Graphs

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1605.05273

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

• arxiv: https://arxiv.org/abs/1606.09375
• github: https://github.com/mdeff/cnn_graph
• github: https://github.com/pfnet-research/chainer-graph-cnn

Semi-Supervised Classification with Graph Convolutional Networks

• arxiv: http://arxiv.org/abs/1609.02907
• github: https://github.com/tkipf/gcn
• blog: http://tkipf.github.io/graph-convolutional-networks/

Graph Based Convolutional Neural Network

• intro: BMVC 2016
• arxiv: http://arxiv.org/abs/1609.08965

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

• blog: http://tkipf.github.io/graph-convolutional-networks/

DeepGraph: Graph Structure Predicts Network Growth

• arxiv: https://arxiv.org/abs/1610.06251

Deep Learning with Sets and Point Clouds

• intro: CMU
• arxiv: https://arxiv.org/abs/1611.04500

Deep Learning on Graphs

• lecture: https://figshare.com/articles/Deep_Learning_on_Graphs/4491686

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

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1703.02161

Deep Learning on Graphs with Graph Convolutional Networks

• slides: http://tkipf.github.io/misc/GCNSlides.pdf

Deep Learning on Graphs with Keras

• intro:; Keras implementation of Graph Convolutional Networks
• github: https://github.com/tkipf/keras-gcn

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

https://arxiv.org/abs/1708.04675

Graph Attention Networks

• intro: ICLR 2018
• intro: University of Cambridge & Centre de Visio per Computador, UAB & Montreal Institute for Learning Algorithms
• project page: http://petar-v.com/GAT/
• arxiv: https://arxiv.org/abs/1710.10903
• github: https://github.com/PetarV-/GAT

Residual Gated Graph ConvNets

https://arxiv.org/abs/1711.07553

Probabilistic and Regularized Graph Convolutional Networks

• intro: CMU
• arxiv: https://arxiv.org/abs/1803.04489

Videos as Space-Time Region Graphs

https://arxiv.org/abs/1806.01810

Relational inductive biases, deep learning, and graph networks

• intro: DeepMind & Google Brain & MIT & University of Edinburgh
• arxiv: https://arxiv.org/abs/1806.01261

Can GCNs Go as Deep as CNNs?

• project: https://sites.google.com/view/deep-gcns
• arxiv: https://arxiv.org/abs/1904.03751
• slides: https://docs.google.com/presentation/d/1L82wWymMnHyYJk3xUKvteEWD5fX0jVRbCbI65Cxxku0/edit#slide=id.p
• github(official, TensorFlow): https://github.com/lightaime/deep_gcns

GMNN: Graph Markov Neural Networks

• intro: ICML 2019
• ariv: https://arxiv.org/abs/1905.06214
• github: https://github.com/DeepGraphLearning/GMNN

DeepGCNs: Making GCNs Go as Deep as CNNs

• intro: ICCV 2019 Oral
• arxiv: https://arxiv.org/abs/1910.06849
• github: https://github.com/lightaime/deep_gcns_torch
• github: https://github.com/lightaime/deep_gcns

Rethinking pooling in graph neural networks

• intro: NeurIPS 2020
• arxiv: https://arxiv.org/abs/2010.11418

Generative Adversarial Networks

Generative Adversarial Nets

• arxiv: http://arxiv.org/abs/1406.2661
• paper: https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
• github: https://github.com/goodfeli/adversarial
• github: https://github.com/aleju/cat-generator

Adversarial Feature Learning

• intro: ICLR 2017
• arxiv: https://arxiv.org/abs/1605.09782
• github: https://github.com/jeffdonahue/bigan

Generative Adversarial Networks

• intro: by Ian Goodfellow, NIPS 2016 tutorial
• arxiv: https://arxiv.org/abs/1701.00160
• slides: http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf
• mirror: https://pan.baidu.com/s/1gfBNYW7

Adversarial Examples and Adversarial Training

• intro: NIPS 2016, Ian Goodfellow OpenAI
• slides: http://www.iangoodfellow.com/slides/2016-12-9-AT.pdf

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

• github: https://github.com/soumith/ganhacks

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

• intro: CatGAN
• arxiv: http://arxiv.org/abs/1511.06390

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

• intro: DCGAN
• arxiv: http://arxiv.org/abs/1511.06434
• github: https://github.com/jazzsaxmafia/dcgan_tensorflow
• github: https://github.com/Newmu/dcgan_code
• github: https://github.com/mattya/chainer-DCGAN
• github: https://github.com/soumith/dcgan.torch
• github: https://github.com/carpedm20/DCGAN-tensorflow

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

• arxiv: https://arxiv.org/abs/1606.03657
• github: https://github.com/openai/InfoGAN
• github(Tensorflow): https://github.com/buriburisuri/supervised_infogan

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

• blog: https://medium.com/@awjuliani/learning-interpretable-latent-representations-with-infogan-dd710852db46#.r0kur3aum
• github: https://gist.github.com/awjuliani/c9ecd8b37d33d6855cd4ed9aa16ce89f#file-infogan-tutorial-ipynb

Coupled Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1606.07536

Energy-based Generative Adversarial Network

• intro: EBGAN
• author: Junbo Zhao, Michael Mathieu, Yann LeCun
• arxiv: http://arxiv.org/abs/1609.03126
• github(Tensorflow): https://github.com/buriburisuri/ebgan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

• github: https://github.com/LantaoYu/SeqGAN

Connecting Generative Adversarial Networks and Actor-Critic Methods

• arxiv: https://arxiv.org/abs/1610.01945

Generative Adversarial Nets from a Density Ratio Estimation Perspective

• arxiv: https://arxiv.org/abs/1610.02920

Unrolled Generative Adversarial Networks

• paper: http://openreview.net/pdf?id=BydrOIcle
• github: https://github.com/bstriner/keras-adversarial

Generative Adversarial Networks as Variational Training of Energy Based Models

• arxiv: https://arxiv.org/abs/1611.01799
• github: https://github.com/Shuangfei/vgan

Multi-class Generative Adversarial Networks with the L2 Loss Function

Least Squares Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1611.04076

Inverting The Generator Of A Generative Adversarial Networ

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1611.05644

ml4a-invisible-cities

• project page: https://opendot.github.io/ml4a-invisible-cities/
• arxiv: https://github.com/opendot/ml4a-invisible-cities

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1611.06430

Associative Adversarial Networks

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1611.06953

Temporal Generative Adversarial Nets

• arxiv: https://arxiv.org/abs/1611.06624

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

• intro: Constructive Machine Learning Workshop (CML) at NIPS 2016
• project page: http://mogren.one/publications/2016/c-rnn-gan/
• arxiv: https://arxiv.org/abs/1611.09904
• github: https://github.com/olofmogren/c-rnn-gan

Ensembles of Generative Adversarial Networks

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1612.00991

Improved generator objectives for GANs

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1612.02780

Stacked Generative Adversarial Networks

• intro: SGAN
• arxiv: https://arxiv.org/abs/1612.04357
• github: https://github.com/xunhuang1995/SGAN

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

• intro: Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1612.05424

AdaGAN: Boosting Generative Models

• intro: Max Planck Institute for Intelligent Systems & Google Brain
• arxiv: https://arxiv.org/abs/1701.02386

Towards Principled Methods for Training Generative Adversarial Networks

• intro: Courant Institute of Mathematical Sciences & Facebook AI Research
• arxiv: https://arxiv.org/abs/1701.04862

Wasserstein GAN

• intro: Courant Institute of Mathematical Sciences & Facebook AI Research
• arxiv: https://arxiv.org/abs/1701.07875
• github: https://github.com/martinarjovsky/WassersteinGAN
• github: https://github.com/Zardinality/WGAN-tensorflow
• github(Tensorflow/Keras): https://github.com/kuleshov/tf-wgan
• github: https://github.com/shekkizh/WassersteinGAN.tensorflow
• gist: https://gist.github.com/soumith/71995cecc5b99cda38106ad64503cee3
• reddit: https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/

Improved Training of Wasserstein GANs

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1704.00028
• github(TensorFlow): https://github.com/igul222/improved_wgan_training
• github: https://github.com/jalola/improved-wgan-pytorch

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

• arxiv: https://arxiv.org/abs/1704.03971
• github: https://github.com/stormraiser/GAN-weight-norm

Controllable Generative Adversarial Network

• intro: Korea University
• arxiv: https://arxiv.org/abs/1708.00598

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

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1711.10337
• reddit: https://www.reddit.com/r/MachineLearning/comments/7gwip3/d_googles_large_scale_gantuning_paper_unfairly/

GAGAN: Geometry-Aware Generative Adverserial Networks

https://arxiv.org/abs/1712.00684

CycleGAN: a Master of Steganography

• intro: NIPS 2017, workshop on Machine Deception
• arxiv: https://arxiv.org/abs/1712.02950

PacGAN: The power of two samples in generative adversarial networks

• intro: CMU & University of Illinois at Urbana-Champaign
• arxiv: https://arxiv.org/abs/1712.04086

ComboGAN: Unrestrained Scalability for Image Domain Translation

• arxiv: https://arxiv.org/abs/1712.06909
• github: https://github.com/AAnoosheh/ComboGAN

Decoupled Learning for Conditional Adversarial Networks

https://arxiv.org/abs/1801.06790

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

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1802.00771

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

• intro: ICLR 2018
• arxiv: https://arxiv.org/abs/1805.03644
• github: https://github.com/BorealisAI/bre-gan

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

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1807.04720
• github: https://github.com/google/compare_gan

Which Training Methods for GANs do actually Converge?

• intro: ICML 2018. MPI Tübingen & Microsoft Research
• project page: https://avg.is.tuebingen.mpg.de/publications/meschedericml2018
• paper: https://avg.is.tuebingen.mpg.de/uploads_file/attachment/attachment/424/Mescheder2018ICML.pdf
• github: https://github.com/LMescheder/GAN_stability

Convergence Problems with Generative Adversarial Networks (GANs)

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1806.11382

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

• intro: CVPR 2017
• project page: https://phillipi.github.io/pix2pix/
• arxiv: https://arxiv.org/abs/1611.07004
• github: https://github.com/phillipi/pix2pix
• github(TensorFlow): https://github.com/yenchenlin/pix2pix-tensorflow
• github(Chainer): https://github.com/mattya/chainer-pix2pix
• github(PyTorch): https://github.com/mrzhu-cool/pix2pix-pytorch
• github(Chainer): https://github.com/wuhuikai/chainer-pix2pix

Remastering Classic Films in Tensorflow with Pix2Pix

• blog: https://hackernoon.com/remastering-classic-films-in-tensorflow-with-pix2pix-f4d551fa0503#.6dmahnt8n
• github: https://github.com/awjuliani/Pix2Pix-Film
• model: https://drive.google.com/file/d/0B8x0IeJAaBccNFVQMkQ0QW15TjQ/view

Image-to-Image Translation in Tensorflow

• blog: http://affinelayer.com/pix2pix/index.html
• github: https://github.com/affinelayer/pix2pix-tensorflow

webcam pix2pix

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

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

• intro: Imperial College London & Indian Institute of Technology
• arxiv: https://arxiv.org/abs/1701.02676

Unsupervised Image-to-Image Translation Networks

• intro: NIPS 2017 Spotlight
• intro: unsupervised/unpaired image-to-image translation using coupled GANs
• project page: http://research.nvidia.com/publication/2017-12_Unsupervised-Image-to-Image-Translation
• arxiv: https://arxiv.org/abs/1703.00848
• github: https://github.com/mingyuliutw/UNIT

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

• intro: UC Berkeley
• project page: https://junyanz.github.io/CycleGAN/
• arxiv: https://arxiv.org/abs/1703.10593
• github(official, Torch): https://github.com/junyanz/CycleGAN
• github(official, PyTorch): https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
• github(PyTorch): https://github.com/eveningglow/semi-supervised-CycleGAN
• github(Chainer): https://github.com/Aixile/chainer-cyclegan

CycleGAN and pix2pix in PyTorch

• intro: Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more)
• github: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

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

• intro: IST Austria & Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1711.05139

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

• intro: Korea University & Clova AI Research
• arxiv: https://arxiv.org/abs/1711.09020
• github: https://github.com//yunjey/StarGAN

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

https://arxiv.org/abs/1711.09554

Toward Multimodal Image-to-Image Translation

• intro: NIPS 2017. BicycleGAN
• project page: https://junyanz.github.io/BicycleGAN/
• arxiv: https://arxiv.org/abs/1711.11586
• github(official, PyTorch): https://github.com//junyanz/BicycleGAN
• github: https://github.com/gitlimlab/BicycleGAN-Tensorflow
• github: https://github.com/kvmanohar22/img2imgGAN
• github: https://github.com/eveningglow/BicycleGAN-pytorch

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

• intro: NVIDIA Corporation, UC Berkeley
• project page: https://tcwang0509.github.io/pix2pixHD/
• arxiv: https://arxiv.org/abs/1711.11585
• github: https://github.com/NVIDIA/pix2pixHD
• youtube: https://www.youtube.com/watch?v=3AIpPlzM_qs&feature=youtu.be

On the Effectiveness of Least Squares Generative Adversarial Networks

https://arxiv.org/abs/1712.06391

GANs for Limited Labeled Data

• intro: Ian Goodfellow
• slides: http://www.iangoodfellow.com/slides/2017-12-09-label.pdf

Defending Against Adversarial Examples

• intro: Ian Goodfellow
• slides: http://www.iangoodfellow.com/slides/2017-12-08-defending.pdf

Conditional Image-to-Image Translation

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1805.00251

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

• arxiv: https://arxiv.org/abs/1808.06601
• github: https://github.com/NVIDIA/vid2vid

Segmentation Guided Image-to-Image Translation with Adversarial Networks

https://arxiv.org/abs/1901.01569

Projects

Generative Adversarial Networks with Keras

• github: https://github.com/phreeza/keras-GAN

Generative Adversarial Network Demo for Fresh Machine Learning #2

• youtube: https://www.youtube.com/watch?v=deyOX6Mt_As&feature=em-uploademail
• github: https://github.com/llSourcell/Generative-Adversarial-Network-Demo
• demo: http://cs.stanford.edu/people/karpathy/gan/

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

• github: https://github.com/AustinStoneProjects/TextGAN

cleverhans v0.1: an adversarial machine learning library

• arxiv: https://arxiv.org/abs/1610.00768
• github: https://github.com/openai/cleverhans

Deep Convolutional Variational Autoencoder w/ Adversarial Network

• intro: An implementation of the deep convolutional generative adversarial network, combined with a varational autoencoder
• github: https://github.com/staturecrane/dcgan_vae_torch

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

• github: https://github.com/255BITS/HyperGAN

AdaGAN: Boosting Generative Models

• intro: AdaGAN: greedy iterative procedure to train mixtures of GANs
• intro: Max Planck Institute for Intelligent Systems & Google Brain
• arxiv: https://arxiv.org/abs/1701.02386
• github: https://github.com/tolstikhin/adagan

TensorFlow-GAN (TFGAN)

• intro: TFGAN: A Lightweight Library for Generative Adversarial Networks
• github: https://github.com//tensorflow/tensorflow/tree/master/tensorflow/contrib/gan
• blog: https://research.googleblog.com/2017/12/tfgan-lightweight-library-for.html

Blogs

Generative Adversial Networks Explained

• blog: http://kvfrans.com/generative-adversial-networks-explained/

Generative Adversarial Autoencoders in Theano

• blog: https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/
• github: https://github.com/mikesj-public/dcgan-autoencoder

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

• blog: http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
• github: https://github.com/AYLIEN/gan-intro

Difficulties training a Generative Adversarial Network

• github: https://github.com/shekkizh/neuralnetworks.thought-experiments/blob/master/Generative%20Models/GAN/Readme.md

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

• blog: https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39#.rpiunhdjh
• gist: https://gist.github.com/awjuliani/8ebf356d03ffee139659807be7fa2611

Deep Learning Research Review Week 1: Generative Adversarial Nets

• blog: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-1-Generative-Adversarial-Nets

Stability of Generative Adversarial Networks

• blog: http://www.araya.org/archives/1183

Instance Noise: A trick for stabilising GAN training

• blog: http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/

Generating Fine Art in 300 Lines of Code

• intro: DCGAN
• blog: https://medium.com/@richardherbert/generating-fine-art-in-300-lines-of-code-4d37218216a6#.63qm8ef9g

Talks / Videos

Generative Adversarial Network visualization

• youtube: https://www.youtube.com/watch?v=mObnwR-u8pc

Resources

The GAN Zoo

• intro: A list of all named GANs!
• github: https://github.com/hindupuravinash/the-gan-zoo

AdversarialNetsPapers: The classical Papers about adversial nets

• github: https://github.com/zhangqianhui/AdversarialNetsPapers

GAN Timeline

• intro: A timeline showing the development of Generative Adversarial Networks (GAN)
• github: https://github.com//dongb5/GAN-Timeline