A Collection of Variational Autoencoders (VAE) in PyTorch.

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

Experiments for understanding disentanglement in VAE latent representations

Pytorch implementation of β-VAE

Dataset to assess the disentanglement properties of unsupervised learning methods

Easy generative modeling in PyTorch

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

Replicating "Understanding disentangling in β-VAE"

Repository for implementation of generative models with Tensorflow 1.x

Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"

Predicting image frames using autoencoder and LSTM

Official PyTorch implementation on ID-GAN: High-Fidelity Synthesis with Disentangled Representation by Lee et al., 2020.

An implementation of Denoising Variational AutoEncoder with Topological loss

Code from the article: "The Role of Disentanglement in Generalisation" (ICLR, 2021).

Pytorch implementation of SCAN: Learning Hierarchical Compositional Visual Concepts, Higgins et al., ICLR 2018

Disentangling the latent space of a VAE.

Implementation of VAE in pytorch.

Variational Autoencoder and a Disentangled version (beta-VAE) implementation in PyTorch-Lightning