Representation Learning via Consistent Assignment of Views to Clusters

• URL: http://arxiv.org/abs/2112.15421v1
• Date: Fri, 31 Dec 2021 12:59:23 GMT
• Title: Representation Learning via Consistent Assignment of Views to Clusters
• Authors: Thalles Silva and Ad\'in Ram\'irez Rivera
• Abstract summary: Consistent Assignment for Representation Learning (CARL) is an unsupervised learning method to learn visual representations. By viewing contrastive learning from a clustering perspective, CARL learns unsupervised representations by learning a set of general prototypes. Unlike contemporary work on contrastive learning with deep clustering, CARL proposes to learn the set of general prototypes in an online fashion.
• Score: 0.7614628596146599
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce Consistent Assignment for Representation Learning (CARL), an unsupervised learning method to learn visual representations by combining ideas from self-supervised contrastive learning and deep clustering. By viewing contrastive learning from a clustering perspective, CARL learns unsupervised representations by learning a set of general prototypes that serve as energy anchors to enforce different views of a given image to be assigned to the same prototype. Unlike contemporary work on contrastive learning with deep clustering, CARL proposes to learn the set of general prototypes in an online fashion, using gradient descent without the necessity of using non-differentiable algorithms or K-Means to solve the cluster assignment problem. CARL surpasses its competitors in many representations learning benchmarks, including linear evaluation, semi-supervised learning, and transfer learning.

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