Pytorch implementation of Wasserstein GANs with Gradient Penalty

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.

A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys

Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.

GANs: Losses, Regularizations and Normalizations

My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.

Wasserstein GAN with Gradient Penalty in DL4S

PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512

Generating shoes with GANs in sake of lulz and education

Major GANs are implemented in this repository 🔥

Tensorflow implementation for training GANs with various objectives and gradient penalties, different network architectures, both image and word generations

A brief visualization of how GP(Gradient Penalty) for GAN works

Image to Image translation using conditional GANs with Wasserstein loss and gradient penalty

LSTM-based GAN for simulating DNA sequence evolution

This project demonstrates a GAN built with PyTorch, using a subset of 5000 CelebA images. It leverages Wasserstein GAN with Gradient Penalty (WGAN-GP) for facial image generation. The provided models are trained for 200 epochs, showcasing integration of techniques from key research papers. Deeper Networks and more Training can improve results.