A Collection of Variational Autoencoders (VAE) in PyTorch.

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

Implementation of mutual learning model between VAE and GMM.

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).

Implementation of LiteVAE

Symbol emergence using Variational Auto-Encoder and Gaussian Mixture Model (Inter-GMM-VAE)~VAEを活用した実画像からの記号創発~

Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE

Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

Pytorch版本实现的VAE和公式推导

A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space

Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow

Implementation of VAEs (Variational Autoencoders) with PyTorch.

Variational Autoencoder (VAE) trained on MNIST

A PyTorch implementation of multimodal VRNN and VAE.

The CNN implementation to qualify images. This repo also contains Japanese coin validation(with binaries) and MNIST challenge detection.

Implementing a Conditional VAE for video prediction with PyTorch

This repository contains the code, data and scripts used to write the Bachelor Thesis "Latent representations for traditional music analysis and generation".

Basic implementation of VAE