Spatial Broadcast Decoder implementation in PyTorch on top of Docker.
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Updated
Jan 21, 2022 - Python
Spatial Broadcast Decoder implementation in PyTorch on top of Docker.
Official implementation of the paper "Learning Invariance Manifolds of Visual Sensory Neurons".
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
Code corresponding to the DiCyR ICLR submission, used for my first research project at ISAE-Supaero.
Improving Disentangled Representatoin Learning with the Beta Bernoulli Process. ICDM 2019.
Object-Centric Disentangled Mechanisms
Learning alternative disentangled representations using weak labels
Learning Face Recognition Unsupervisedly by Disentanglement and Self-Augmentation (ICRA 2020)
Improve generalizability of CNN-based vision models to downstream tasks by disentangling learned representation space dimensions
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)
PyTorch version of disentanglement lib
Single-Cell (Perturbation) Model Library
Making locally disentangled vaes.
training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Code for Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement
Official tensorflow implementation of "Demystifying Inter-Class Disentanglement", ICLR 2020.
Disentangling the latent space of a VAE.
Pytorch implementation of Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE paper
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