# DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors

arXiv: https://arxiv.org/abs/2006.05895 · HTML: https://mlsanity.com/paper/2006.05895/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Emotionally Enhanced Talking Face Generation](https://mlsanity.com/paper/2303.11548/) (2023)
- [Disentangling speech from surroundings with neural embeddings](https://mlsanity.com/paper/2203.15578/) (2022)

## Abstract (arXiv)

Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task. Models that can disentangle attributes well provide greater interpretability and control. In this paper, we propose a self-supervised framework DisCont to disentangle multiple attributes by exploiting the structural inductive biases within images. Motivated by the recent surge in contrastive learning paradigms, our model bridges the gap between self-supervised contrastive learning algorithms and unsupervised disentanglement. We evaluate the efficacy of our approach, both qualitatively and quantitatively, on four benchmark datasets.