Comprehensive Guide to Learning Resources for GANs

[ Guide ]Want to learn everything about GANs?Someone has already organized it for you!From paper resources to application examples, to books, tutorials, and beginner guides, whether you are a newcomer or an expert, you will find something useful.

This article is a categorized compilation of open-source resources related to GANs.The full text is divided into five sections: papers, applications, courses, books, and beginner guides, with the more technical papers and application examples placed at the front, while courses and beginner guides are at the end.

Whether you are a beginner or an expert, I believe the content of this article will be helpful to you.For the papers and applications section, generally, the paper link is provided first, followed by the GitHub software resources.

This section lists some core papers related to GANs.First, it presents the foundational paper that proposes and establishes the basic concept of GANs.Then, it sequentially categorizes and introduces papers on some common variants of GANs.

Laplacian Pyramid Adversarial Network (LAPGAN)

• Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks

http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf

https://github.com/witnessai/LAPGAN

Deep Convolutional GAN (DCGAN)

• Deep Convolutional Generative Adversarial Networks

http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf

https://github.com/witnessai/LAPGAN

• Generative Adversarial Text to Image Synthesis

https://arxiv.org/pdf/1605.05396.pdf

https://github.com/reedscot/icml2016

Adversarial Autoencoders (AAE)

• Adversarial Autoencoders

https://arxiv.org/abs/1511.05644

https://github.com/Naresh1318/Adversarial_Autoencoder

Generative Recurrent Adversarial Network (GRAN)

• Generating Images with Recurrent Adversarial Networks

https://arxiv.org/abs/1602.05110

https://github.com/jiwoongim/GRAN

Information Maximizing GAN (InfoGAN)

• InfoGAN: Information Maximizing GANs

http://papers.nips.cc/paper/6399-infogan-interpretable-representation

https://github.com/openai/InfoGAN

Theory and Training of GANs

• Energy-based Generative Adversarial Network

https://arxiv.org/pdf/1609.03126v2.pdf

https://github.com/buriburisuri/ebgan

• Which Training Methods for GANs Do Actually Converge

https://arxiv.org/pdf/1801.04406.pdf

https://github.com/LMescheder/GAN_stability

• Improved Techniques for Training GANs

https://arxiv.org/abs/1609.04468

https://github.com/openai/improved-gan

• Towards Principled Methods for Training Generative Adversarial Networks

https://arxiv.org/abs/1701.04862

• Least Squares Generative Adversarial Networks

https://arxiv.org/abs/1611.04076

https://github.com/pfnet-research/chainer-LSGAN

• Wasserstein GAN

https://arxiv.org/abs/1701.07875

https://github.com/martinarjovsky/WassersteinGAN

• Improved Training of Wasserstein GANs

https://arxiv.org/abs/1704.00028

https://github.com/igul222/improved_wgan_training

• Generalization and Equilibrium in Generative Adversarial Nets

https://arxiv.org/abs/1703.00573

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

http://papers.nips.cc/paper/7240-gans-trained-by-a-two-t

https://github.com/bioinf-jku/TTUR

Image Analysis

• Generative Adversarial Text to Image Synthesis

https://arxiv.org/abs/1605.05396

https://github.com/reedscot/icml201

• Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

https://arxiv.org/abs/1612.00005v1

https://github.com/Evolving-AI-Lab/ppgn

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

https://arxiv.org/abs/1511.06434

https://github.com/jacobgil/keras-dcgan

• Progressive Growing of GANs for Improved Quality, Stability, and Variation

http://research.nvidia.com/publication/2017-10_Progressive-Growing-of

https://github.com/tkarras/progressive_growing_of_gans

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

https://arxiv.org/pdf/1612.03242v1.pdf

https://github.com/hanzhanggit/StackGAN

• Self-Attention Generative Adversarial Networks

https://arxiv.org/abs/1805.08318

https://github.com/heykeetae/Self-Attention-GAN

• Large Scale GAN Training for High Fidelity Natural Image Synthesis

https://arxiv.org/abs/1809.11096

Image-to-Image Translation

• Image-to-image Translation Using Conditional Adversarial Nets

https://arxiv.org/pdf/1611.07004v1.pdf

https://github.com/phillipi/pix2pix

• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

https://arxiv.org/abs/1703.05192

https://github.com/carpedm20/DiscoGAN-pytorch

• Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

https://junyanz.github.io/CycleGAN/

https://github.com/junyanz/CycleGAN

• CoGAN: Coupled Generative Adversarial Networks

https://arxiv.org/abs/1606.07536

https://github.com/andrewliao11/CoGAN-tensorflow

• Unsupervised Image-to-Image Translation Networks

https://arxiv.org/abs/1703.00848

• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://arxiv.org/abs/1711.11585

• UNIT: Unsupervised Image-to-image Translation Networks

https://arxiv.org/abs/1703.00848

https://github.com/mingyuliutw/UNIT

• Multimodal Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1804.04732

https://github.com/nvlabs/MUNit

Super Resolution

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://arxiv.org/abs/1609.04802

https://github.com/leehomyc/Photo-Realistic-Super-Resoluton

• High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks

https://arxiv.org/pdf/1707.00737.pdf

• Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-resolution Network

https://arxiv.org/pdf/1811.00344.pdf

https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw

Text-to-Image Translation

• TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network

https://arxiv.org/pdf/1703.06412.pdf

https://github.com/dashayushman/TAC-GAN

• Generative Adversarial Text to Image Synthesis

https://arxiv.org/pdf/1605.05396.pdf

https://github.com/paarthneekhara/text-to-imag

• Learning What and Where to Draw

http://www.scottreed.info/files/nips2016.pdf

https://github.com/reedscot/nips2016

Image Editing

• Invertible Conditional GANs for Image Editing

https://arxiv.org/pdf/1611.06355.pdf

https://github.com/Guim3/IcGAN

• Image De-raining Using a Conditional Generative Adversarial Network

https://arxiv.org/abs/1701.05957

https://github.com/hezhangsprinter/ID-CGAN

Other Applications

• Generating Multi-label Discrete Patient Records Using Generative Adversarial Networks

https://arxiv.org/abs/1703.06490

https://github.com/mp2893/medgan

• Adversarial Generation of Natural Language

https://arxiv.org/abs/1705.10929

• Language Generation with Recurrent Generative Adversarial Networks Without Pre-training

https://arxiv.org/abs/1706.01399

https://github.com/amirbar/rnn.wgan

• Adversarial Ranking for Language Generation

http://papers.nips.cc/paper/6908-adversarial-ranking-for-language-generation

https://github.com/desire2020/RankGAN

• Adversarial Training Methods for Semi-Supervised Text Classification

https://arxiv.org/abs/1605.07725

https://github.com/aonotas/adversarial_text

• Deep Learning: GANs and Variational Autoencoders by Udemy:

https://www.udemy.com/deep-learning-gans-and-variational-autoencoders/

Differentiable Inference and Generative Models by the University of

• Toronto:

http://www.cs.toronto.edu/~duvenaud/courses/csc2541/

• Learning Generative Adversarial Networks by Udemy:

https://www.udemy.com/learning-generative-adversarial-networks/

• GANs in Action – Deep Learning with Generative Adversarial Networks by Manning Publications:

https://www.manning.com/books/gans-in-action

• GANs from Scratch 1: A Deep Introduction

https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f

• Keep Calm and Train a GAN. Pitfalls and Tips on Training Generative Adversarial Networks:

https://medium.com/@utk.is.here/keep-calm-and-train-a-gan-pitfalls-and-tips-on-training-generative-adversarial-networks-edd529764aa9

• CVPR 2018 Tutorial on GANs:

https://sites.google.com/view/cvpr2018tutorialongans/

• Introductory Guide to Generative Adversarial Networks (GANs) and Their Promise!:

https://www.analyticsvidhya.com/blog/2017/06/introductory-generative-adversarial-networks-gans/

• Generative Adversarial Networks for Beginners:

https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners

• Understanding and Building Generative Adversarial Networks (GANs):

https://becominghuman.ai/understanding-and-building-generative-adversarial-networks-gans-8de7c1dc0e25

Reference Links:

https://machinelearningmindset.com/generative-adversarial-networks-roadmap/