Generating shoes with GANs in sake of lulz and education

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

GANs: Losses, Regularizations and Normalizations

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

Major GANs are implemented in this repository 🔥

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

Pytorch implementation of Wasserstein GANs with Gradient Penalty

Wasserstein GAN with Gradient Penalty in DL4S

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

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

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

LSTM-based GAN for simulating DNA sequence evolution

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

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

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

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.