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.

Notes about the video on the Variational Autoencoder

Variational Autoencoder (VAE)-based molecular SMILES string generator

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