A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
Jun 13, 2024 - Python
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:https://arxiv.org/abs/1802.05983)
Replicating "Understanding disentangling in β-VAE"
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"
An implementation of Denoising Variational AutoEncoder with Topological loss
Official PyTorch implementation on ID-GAN: High-Fidelity Synthesis with Disentangled Representation by Lee et al., 2020.
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.
Variational Autoencoder and a Disentangled version (beta-VAE) implementation in PyTorch-Lightning
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