# A Closer Look at Disentangling in β-VAE

arXiv: https://arxiv.org/abs/1912.05127 · HTML: https://mlsanity.com/paper/1912.05127/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models](https://mlsanity.com/paper/2407.18999/) (2024)
- [Towards Flexibility and Interpretability of Gaussian Process State-Space Model](https://mlsanity.com/paper/2301.08843/) (2023)
- [Disentanglement Learning via Topology](https://mlsanity.com/paper/2308.12696/) (2023)
- [Reproducible, incremental representation learning with Rosetta VAE](https://mlsanity.com/paper/2201.05206/) (2022)
- [Feature Alignment as a Generative Process](https://mlsanity.com/paper/2106.12562/) (2021)
- [Smooth InfoMax -- Towards Easier Post-Hoc Interpretability](https://mlsanity.com/paper/2408.12936/) (2024)
- [A Generative Model for Disentangling Galaxy Photometric Parameters](https://mlsanity.com/paper/2507.15898/) (2025)

## Abstract (arXiv)

In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating factors are also statistically independent, disentangled representations can be formed by Bayesian inference of latent variables. We examine a generalization of the Variational Autoencoder (VAE), β-VAE, for learning such representations using variational inference. β-VAE enforces conditional independence of its bottleneck neurons controlled by its hyperparameter β. This condition is in general not compatible with the statistical independence of latents. By providing analytical and numerical arguments, we show that this incompatibility leads to a non-monotonic inference performance in β-VAE with a finite optimal β.