[TPAMI 2022] GAN Inversion: A Survey

Measures and metrics for image2image tasks. PyTorch.

Official implementation of the paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis" in ICLR 2021

Generator loss to reduce mode-collapse and to improve the generated samples quality.

A mix of GAN implementations including progressive growing

MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Tooling for GANs in TensorFlow

Awesome GAN for Medical Imaging

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Implementation of "Autoencoding beyond pixels using a learned similarity metric" using Pytorch

High-fidelity performance metrics for generative models in PyTorch

Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Conditional GAN for generating synthetic tabular data.

Official repository for the paper "Instance-Conditioned GAN" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.

This repo implements GAN-based models for Dialogue Generation (DP-GAN, SeqGAN, and our own proposed DPAC-GAN)

BourGAN: Generative Networks with Metric Embeddings

This repository has the codebase and results obtained for the calculation of the GM Score for GAN evaluation.

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

Are High-Frequency Components Beneficial for Training of Generative Adversarial Networks

Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text (EMNLP2018)

Code for paper "Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave System"s

Semi-supervised GAN in "Improved Techniques for Training GANs"

PyTorch implementation of the REMIND method from our ECCV-2020 paper "REMIND Your Neural Network to Prevent Catastrophic Forgetting"

Easy generative modeling in PyTorch

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset