[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).

🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...

This repository contains code and bonus content which will be added from time to time for the books "Learning Generative Adversarial Network- GAN" and "R Data Analysis Cookbook - 2nd Edition" by Packt

GAN, SSGAN, WGAN, and VAE are neural networks for content generation. GAN generates realistic images, SSGAN improves quality, WGAN ensures stability, and VAE compresses data to learn features. Applications include image generation, quality enhancement, and fraud detection.