Popular GitHub Repositories for Generative Adversarial Networks

Keras implementations of Generative Adversarial Networks.

Learning Chinese Character style with conditional GAN

A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun

Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks

Research Framework for easy and efficient training of GANs based on Pytorch

Generative Adversarial Transformers

[IEEE TIP] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

Generative Adversarial Networks (GANs) resources sorted by citations

Speech Enhancement Generative Adversarial Network in TensorFlow

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

Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression

Project Page of 'Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks'

Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'

CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021

Pytorch implementation of Generated Image Quality Assessment

This is a pix2pix demo that learns from pose and translates this into a human. A webcam-enabled application is also provided that translates your pose to the trained pose. Everybody dance now !

Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

Official PyTorch implementation of GDWCT (CVPR 2019, oral)

Periodic Spatial Generative Adversarial Networks

[CVPR 2020] Official Implementation: "Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models".