Projects sharing features with Vegans

Catalyst.GAN

Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Code for the paper "Adversarial Generator-Encoder Networks"

Learning deep representations by mutual information estimation and maximization

PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Evaluating Lossy Compression Rates of Deep Generative Models

A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator

Pytorch Implementation of CGAN

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

simple generative adversarial network (GAN) using PyTorch

Wasserstein GAN

Pytorch implementation of Block Neural Autoregressive Flow

pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro

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

Bayesian optimization in PyTorch

Implementation A Style-Based Generator Architecture for Generative Adversarial Networks in PyTorch