# Disentangled Representation Learning Using (β-)VAE and GAN

arXiv: https://arxiv.org/abs/2208.04549 · HTML: https://mlsanity.com/paper/2208.04549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Isolating Sources of Disentanglement in Variational Autoencoders](https://mlsanity.com/paper/1802.04942/) (2018)
- [Deep Convolutional Inverse Graphics Network](https://mlsanity.com/paper/1503.03167/) (2015)
- [Old Photo Restoration via Deep Latent Space Translation](https://mlsanity.com/paper/2009.07047/) (2020)
- [High-Fidelity Synthesis with Disentangled Representation](https://mlsanity.com/paper/2001.04296/) (2020)
- [Demystifying Inductive Biases for β-VAE Based Architectures](https://mlsanity.com/paper/2102.06822/) (2021)
- [β-VAEs can retain label information even at high compression](https://mlsanity.com/paper/1812.02682/) (2018)

## Abstract (arXiv)

Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provided the desired features for the required experiments in this research. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension. Note that the GAN was used to improve the quality of output image reconstruction.