A Roadmap to Learn Generative Adversarial Networks

GANs World Resources

Table of Contents

The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Generative Adversarial Networks .

There are different motivations for this open source project.

What's the point of this open source project?

The organization of the resources is such that the user can easily find the things he/she is looking for. We divided the resources to a large number of categories that in the beginning one may have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. Even if someone doesn't know what to look for, in the beginning, the list of resources have been provided.

This chapter is associated with the papers published associated with generative adversarial Networks.

Image by: Rouzbeh Asghari Shirvani

Core: Generative Adversarial Networks (VanillaGAN)

Title Text Software Citation Generative Adversarial Nets Paper Code Rate :star2: :star2: :star2: :star2: :star2: ENERGY-BASED GENERATIVE ADVERSARIAL NETWORK Paper Code Rate :star2: :star2: :star2: :star2: Which Training Methods for GANs do Actually Converge Paper Code Rate :star2: :star2:

Conditional Generative Adversarial Networks (CGAN)

Title Text Software Citation Conditional generative adversarial nets Paper Code Rate :star2: :star2: :star2: :star2: :star2: Photo-realistic single image super-resolution using a GAN Paper Code Rate :star2: :star2: :star2: :star2: Image-to-Image Translation with Conditional Adversarial Networks Paper Code Rate :star2: :star2: :star2: :star2: Generative Visual Manipulation on the Natural Image Manifold Paper Code Rate :star2: :star2:

Laplacian Pyramid of Adversarial Networks (LAPGAN)

Title Text Software Citation Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Paper Code Rate :star2: :star2: :star2: :star2: :star2:

Deep Convolutional Generative Adversarial Networks (DCGAN)

Title Text Software Citation Deep Convolutional Generative Adversarial Networks Paper Code Rate :star2: :star2: :star2: :star2: :star2: Generative Adversarial Text to Image Synthesis Paper Code Rate :star2: :star2: :star2:

Adversarial Autoencoders (AAE)

Title Text Software Citation Adversarial Autoencoders Paper Code Rate :star2: :star2: :star2: :star2: :star2:

Generative Recurrent Adversarial Networks (GRAN)

Title Text Software Citation Generating images with recurrent adversarial networks Paper Code Rate :star2: :star2: :star2: :star2:

Information Maximizing Generative Adversarial Networks (InfoGan)

Title Text Software Citation Infogan: Information maximizing GANs Paper Code Rate :star2: :star2: :star2: :star2: :star2:

Bidirectional Generative Adversarial Networks (BiGan)

Title Text Software Citation Adversarial feature learning Paper Code Rate :star2: :star2: :star2: :star2: :star2:

GANs Theory and Training

Title Text Software Energy-based generative adversarial network Paper Code Which Training Methods for GANs do actually Converge Paper Code Improved Techniques for Training GANs Paper Code Towards Principled Methods for Training Generative Adversarial Networks Paper Least Squares Generative Adversarial Networks Paper Code Wasserstein GAN Paper Code Improved Training of Wasserstein GANs Paper Code Generalization and Equilibrium in Generative Adversarial Nets Paper GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Paper Code Spectral Normalization for Generative Adversarial Networks Paper Code Title Text Software Generative Adversarial Text to Image Synthesis Paper Code Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Paper Code Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Paper Code Progressive Growing of GANs for Improved Quality, Stability, and Variation Paper Code StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks Paper Code Self-Attention Generative Adversarial Networks Paper Code Large Scale GAN Training for High Fidelity Natural Image Synthesis Paper

Image-to-image translation

Title Text Software Image-to-image translation using conditional adversarial nets Paper Code Learning to Discover Cross-Domain Relations with Generative Adversarial Networks Paper Code Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Paper Code CoGAN: Coupled Generative Adversarial Networks Paper Code Unsupervised Image-to-Image Translation Networks Paper High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Paper UNIT: UNsupervised Image-to-image Translation Networks Paper Code Multimodal Unsupervised Image-to-Image Translation Paper Code Title Text Software Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Paper Code High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks Paper Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network Paper Code Title Text Software TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network Paper Code Generative Adversarial Text to Image Synthesis Paper Code Learning What and Where to Draw Paper Code Title Text Software Invertible Conditional GANs for image editing Paper Code Image De-raining Using a Conditional Generative Adversarial Network Paper Code Title Text Software Generating multi-label discrete patient records using generative adversarial networks Paper Code Adversarial Generation of Natural Language Paper Language Generation with Recurrent Generative Adversarial Networks without Pre-training Paper Code Adversarial ranking for language generation Paper Code Adversarial Training Methods for Semi-Supervised Text Classification Paper Code

• Deep Learning: GANs and Variational Autoencoders by Udemy: [ Link ]
• Differentiable Inference and Generative Models by the University of Toronto: [ Link ]
• Learning Generative Adversarial Networks by Udemy: [ Link ]

• GANs in Action - Deep learning with Generative Adversarial Networks by manning Publications: [ Link ]

• GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow : [ Link ]
• Keep Calm and train a GAN. Pitfalls and Tips on training Generative Adversarial Networks : [ Link ]
• CVPR 2018 Tutorial on GANs : [ Link ]
• Introductory guide to Generative Adversarial Networks (GANs) and their promise! : [ Link ]
• Generative Adversarial Networks for beginners : [ Link ]
• Understanding and building Generative Adversarial Networks(GANs) : [ Link ]

Please consider the following criterions in order to help us in a better way:

1. The pull request is mainly expected to be a link suggestion.
2. Please make sure your suggested resources are not obsolete or broken.
3. Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
4. Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.