A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
May 6, 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://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"
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
Spatial Broadcast Decoder implementation in PyTorch on top of Docker.
Pytorch implementation of SCAN: Learning Hierarchical Compositional Visual Concepts, Higgins et al., ICLR 2018
Disentangling the latent space of a VAE.
Fancy brand new letters with generative models
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