Experiments for understanding disentanglement in VAE latent representations
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Updated
Feb 2, 2023 - Python
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"
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch
[CVPR2020] Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
🧶 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
Dataset and model for disentangling chat on IRC
The official implementation of "Disentangling Long and Short-Term Interests for Recommendation" (WWW '22)
Time-Lapse Disentanglement With Conditional GANs [SIGGRAPH 2022]
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)
Pytorch implementation of Learning Disentangled Representations via Mutual Information Estimation (ECCV 2020)
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.
[ICML 2020] InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.
Official pytorch implementation of "An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild", NeurIPS 2021.
Dataset to study disentanglement in the context of symbolic music. Published as an ISMIR'20 paper titled: "dMelodies: A Music Dataset for Disentanglement Learning"
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
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