google-research/disentanglement_lib

Health v2 · maintenance only

Flags: archived

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1425 stars · 202 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

disentanglement_lib is an open-source Python library for research on learning disentangled representations, supporting models like BetaVAE, FactorVAE, BetaTCVAE, and DIP-VAE along with standard disentanglement metrics and datasets. It provides a pipeline for model training, postprocessing, evaluation, and visualization, and ships with over 10,000 pretrained disentanglement models.

Use cases

• train and evaluate disentangled representation learning models
• compute disentanglement metrics like MIG, SAP, DCI, and FactorVAE score
• benchmark VAE variants on datasets like dSprites and Shapes3D
• use pretrained disentanglement models for research experiments
• visualize learned latent representations
• reproduce large-scale disentanglement study results

When to choose

• you are doing academic research on disentangled representation learning
• you need standardized implementations of disentanglement metrics and datasets
• you want to compare VAE-based models under a unified framework
• you need pretrained disentanglement models for downstream experiments

When to avoid

• you need production-ready representation learning for applications
• you prefer PyTorch over TensorFlow
• you need the latest actively maintained deep learning tooling
• you work on Windows or non-Linux environments

Facets

library · maturity maintenance

machine-learning deep-learning benchmarking data-science data-visualization machine-learning deep-learning artificial-intelligence computer-vision python disentangled-representations variational-autoencoder tensorflow unsupervised-learning research pretrained-models metrics linux gpu

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