Projects sharing features with Catalyst Team Gan

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

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

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Research Framework for easy and efficient training of GANs based on Pytorch

High-fidelity performance metrics for generative models in PyTorch

This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep

Code for Variational Laplace Autoencoders

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

Collection of generative models in Pytorch version.