Implementing various GAN Architectures in PyTorch.
-
Updated
Oct 18, 2020 - Jupyter Notebook
Implementing various GAN Architectures in PyTorch.
WGAN with feedback from discriminator& LayerNorm instead of BatchNorm
Deep Convolutional Generative Adversarial Network (DCGAN) for generating gemstones.
PyTorch implementation of WGAN-GP-based video generation. Includes functionality for measuring Frechet Video Distance and implementing recent research improvements of WGAN-GP. Read paper at https://github.com/talcron/frame-prediction-pytorch/blob/media/paper.pdf
3 experiments of GAN/wGAN on simple gaussian distribution, MNIST dataset and exploration of music generation by MuseGAN.
Image inpainting
Code to reproduce experiments in "Convergence Properties of Generative Adversarial Networks".
This repository contains some code for demonstrating the application of Wasserstein GANs (WGANs)
A Pytorch Lightning WGAN-gp to generate faces
TensorFlow Generative Adversarial Networks (GANs)
Master's Final Project: Adversarial Domain Adaptation Super Resolution
Project to get attention from discriminator: 1st combination
Generating pokemon (and other things) with GANs
A walkthrough of two GAN implementations (DCGAN and WGAN_GP)
Add a description, image, and links to the wgan topic page so that developers can more easily learn about it.
To associate your repository with the wgan topic, visit your repo's landing page and select "manage topics."