facebookresearch / disentangling-correlated-factors
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.
☆72Updated last year
Alternatives and similar repositories for disentangling-correlated-factors:
Users that are interested in disentangling-correlated-factors are comparing it to the libraries listed below
- Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style☆49Updated 3 years ago
- ☆33Updated 4 years ago
- This repository contains the code for our paper "Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguo…☆39Updated last year
- The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight…☆53Updated 2 years ago
- Code of "Deep invariant networks with differentiable augmentation layers"☆18Updated 2 years ago
- Code for the paper "Contrastive Learning Inverts the Data Generating Process".☆89Updated 5 months ago
- Package for working with hypernetworks in PyTorch.☆121Updated last year
- ☆35Updated last year
- 🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervi…☆127Updated last year
- [NeurIPS 2023] code for "DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models☆60Updated last year
- Transformers with doubly stochastic attention☆44Updated 2 years ago
- ☆163Updated 2 years ago
- Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)☆134Updated 3 years ago
- Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper☆104Updated 3 years ago
- ☆25Updated 3 years ago
- Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"☆40Updated 2 years ago
- ☆108Updated 2 years ago
- Visualizing representations with diffusion based conditional generative model.☆87Updated last year
- Code for the paper: Complex-Valued Autoencoders for Object Discovery☆48Updated last year
- Official implementation for Equivariant Architectures for Learning in Deep Weight Spaces [ICML 2023]☆86Updated last year
- ☆49Updated 3 years ago
- ☆17Updated 5 years ago
- Re-implementation of the StylEx paper, training a GAN to explain a classifier in StyleSpace, paper by Lang et al. (2021).☆36Updated last year
- Code for the paper: Rotating Features for Object Discovery☆49Updated 5 months ago
- Visual Representation Learning Benchmark for Self-Supervised Models☆35Updated 9 months ago
- Personal implementation of ASIF by Antonio Norelli☆25Updated 8 months ago
- Contrastively Disentangled Sequential Variational Audoencoder☆46Updated 3 months ago
- Neural Diffusion Processes☆76Updated 5 months ago
- [NeurIPS 2023] Official Implementation: "Consistent Diffusion Models"☆55Updated last year
- ☆34Updated 3 years ago