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)

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

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)

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.

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

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

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