Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Optimus: the first large-scale pre-trained VAE language model

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

Official PyTorch implementation of A Quaternion-Valued Variational Autoencoder (QVAE).

Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

🤖 | Learning PyTorch through official examples

Dirichlet-Variational Auto-Encoder by PyTorch

Codebase for the paper: Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts

A Variational Autoencoder in PyTorch for the CelebA Dataset.

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

Deep Learning And Applied Artificial Intelligence Project 2019/2020 - Molecular Synthesis & Reconstruction

Pytorch implementation of a Variational Autoencoder (VAE) that learns from the MNIST dataset and generates images of altered handwritten digits.

Variational Autoencoder (VAE)-based molecular SMILES string generator

Notes about the video on the Variational Autoencoder

This repository contains code for VAE and CVAE using residual and inverse residual blocks

Mapping properties to molecules in QM7-X

Codes for paper: CVQVAE: A REPRESENTATION LEARNING METHOD FOR MULTI-OMICS SINGLE CELL DATA INTEGRATION