Experiments for understanding disentanglement in VAE latent representations

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Dataset to assess the disentanglement properties of unsupervised learning methods

Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟

Replicating "Understanding disentangling in β-VAE"

Time-Lapse Disentanglement With Conditional GANs [SIGGRAPH 2022]

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

[CVPR2020] Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification

Pytorch implementation of Learning Disentangled Representations via Mutual Information Estimation (ECCV 2020)

The official implementation of "Disentangling Long and Short-Term Interests for Recommendation" (WWW '22)

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)

Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch

This repository summarizes the material gathered for the tutorial on learning disentangled representations in the imaging domain, and serves as a roadmap for the disentanglement aficionados.

Dataset and model for disentangling chat on IRC

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Official code for Interspeech 2023 paper "Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering"

[ICML 2020] InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs

Official implementation of the paper *PDE-Driven Spatiotemporal Disentanglement*