disentanglement_lib is an open-source library for research on learning disentangled representations.

Experiments for understanding disentanglement in VAE latent representations

A curated list of research papers related to learning disentangled representations

Disentanglement library for PyTorch

Replicating "Understanding disentangling in β-VAE"

Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

Official implementation of the paper "Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models

Tensorflow implementation of CVPR 2018 paper "Disentangled Person Image Generation"

Disentagnled Graph Collaborative Filtering, SIGIR2020

🧶 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 "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"

A colorization framework that disentangles the color multimodality and the structural consistency via adaptively located anchors, so that both aspects can be achieved effectively. [SIGGRAPH Asia 2022]

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

Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

Variational Autoencoder for Unsupervised and Disentangled Representation Learning of content and motion features in sequential data (Mandt et al.).

Content and Style Disentanglement for Artistic Style Transfer [ICCV19]

No description available

[ACL'25] We propose a novel fine-tuning method, Separate Memory and Reasoning, which combines prompt tuning with LoRA.

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

No description available

A benchmarking suite for disentanglement algorithms, suited for evaluating robustness to correlated factors. Codebase for the paper "Disentanglement of Correlated Factors via Hausdorff Factorized Support" by Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt.

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Disentangling Multiple Conditional Inputs in GANs

Code of "3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces"

Code for "A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations" (NAACL 2019)

Experiments with beta-VAE to learn disentangled representations from the data

No description available

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.

PyTorch Implementations of a VAE and a beta-VAE.