A Collection of Variational Autoencoders (VAE) in PyTorch.

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

Implementation of a Hybrid Variational Autoencoder (VAE) for label information-guided dimensionality reduction.

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

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

Implementation of LiteVAE

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.

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

Pytorch版本实现的VAE和公式推导

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

Variational Autoencoder (VAE) trained on MNIST

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

A PyTorch implementation of multimodal VRNN and VAE.

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".

Handwritten Digit Generation with VAE and GAN are applied.

A repository for generating synthetic data (images) using various DL/ML models.

Basic implementation of VAE