Experiments for understanding disentanglement in VAE latent representations
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Updated
Feb 2, 2023 - Python
Experiments for understanding disentanglement in VAE latent representations
Pytorch implementation of β-VAE
Dataset to assess the disentanglement properties of unsupervised learning methods
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http:https://arxiv.org/abs/1802.05983)
Replicating "Understanding disentangling in β-VAE"
Deep active inference agents using Monte-Carlo methods
[ICML 2020] InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivalent to optimizing the IMA-regularized log-likelihood under certain assumptions (e.g., small decoder variance).
A TensorFlow implementation of FactorVAE, proposed in "Disentangling by Factorising" by Kim et al.
ML2 Project following ControlVAE: Tuning, Analytical Properties, and Performance Analysis
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