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SimCLR: A Simple Framework for Contrastive Learning of Visual Representations A Simple Framework for Contrastive Learning of Visual Representations [ pdf ] Ting Chen , Simon Kornblith , Mohammad Norouzi , Geoffrey Hinton , ICML 2020 Abstract: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger...
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