Learning Face Recognition Unsupervisedly by Disentanglement and Self-Augmentation (ICRA 2020)
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Updated
Dec 13, 2023
Learning Face Recognition Unsupervisedly by Disentanglement and Self-Augmentation (ICRA 2020)
An Image Dataset for Causal Analysis in Disentangled Representations
Making locally disentangled vaes.
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).
PyTorch version of disentanglement lib
This is the code repository for {Empirical Study on Exploring the Impact of Controlling the Objective on Disentanglement Learning During Training}.
A toolkit to make working with fd3 easier
training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Pytorch implementation of Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE paper
Correlated Ellipses dataset for measuring disentanglement when the factors of variation are correlated. See our paper "Hyperprior Induced Unsupervised Disentanglement of Latent Representations" (AAAI 2019)
Spatial Decomposition Network - TensorFlow
Code corresponding to the DiCyR ICLR submission, used for my first research project at ISAE-Supaero.
Code from the article: "The Role of Disentanglement in Generalisation" (ICLR, 2021).
Improving Disentangled Representatoin Learning with the Beta Bernoulli Process. ICDM 2019.
Object-Centric Disentangled Mechanisms
Learning alternative disentangled representations using weak labels
Single-Cell (Perturbation) Model Library
Spatial Broadcast Decoder implementation in PyTorch on top of Docker.
Official code of "Discover the Unknown Biased Attribute of an Image Classifier" (ICCV 2021)
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