# Disentangling by Factorising

arXiv: https://arxiv.org/abs/1802.05983 · HTML: https://mlsanity.com/paper/1802.05983/

## Key facts

- Cited by 616 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

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## Abstract (arXiv)

We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon β-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.