Video Understanding through the Disentanglement of Appearance and Motion

This is a curated list of papers in related domains, including disentangled representation learning, neural dialogue generation, neural variational models, variational inference etc.

A multimodal dynamical variational autoencoder for audiovisual speech representation learning

Experiments on Disentangled Representation Learning using Variational autoencoding algorithms

PyTorch implementation of "Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer" - tuned version

Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space. MICCAI 2019.

training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

unofficial backup service for mpi3d_real dataset

Pytorch implementation of Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE paper

Learning Object Representations by Mixing Scenes, MSc thesis, University of Bern, Switzerland

Disentangled representation learning model for digital pathology data as a custom similarity metric for deformable image registration.

Applying VAE and DGM families to JATS personality survey database in PyTorch

PyTorch version of disentanglement lib

Temporal Attention Bottleneck for VAE is informative? (ICML 2023)

Vector-Quantised Variational Autoencoder for privacy-preserving speech recognition

Unofficial Knet.jl implementation of paper "Image-to-image Translation via Hierarchical Style Disentanglement" (CVPR 2021 Oral).

BERT EncoderDecoderModel to reproduce a sentence with learned disentangled represntation

To learn and reason like humans, AI must first learn to factorise interpretable representations of independent data generative factors (preferably in an unsupervised manner!!). What does all this mean? Go through this tutorial to get an overview of disentanglement in the context of unsupervised visual disentangled representation learning.

[TNNLS 2022] Pytorch codes for Federated Generalized Face Presentation Attack Detection

ML2 Project following ControlVAE: Tuning, Analytical Properties, and Performance Analysis