DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors release_bqml26d7ozb6dpi64zm5mqjqtm

by Sarthak Bhagat, Vishaal Udandarao, Shagun Uppal

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2020  

Abstract

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.
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Date   2020-06-29
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arXiv  2006.05895v2
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