DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors

Autor:	Bhagat, Sarthak, Udandarao, Vishaal, Uppal, Shagun
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published at the 37th International Conference on Machine Learning (ICML 2020) Workshop on ML Interpretability for Scientific Discovery
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.05895 Zobrazit plný text záznamu View this record from Arxiv