A Simple Framework for Contrastive Learning of Visual Representations
ICML, pp. 1597-1607, 2020.
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 representation...More
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