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Decoupled Contrastive Learning

Contrastive learning is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented views of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart.

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Cite this as

Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann LeCun (2024). Dataset: Decoupled Contrastive Learning. https://doi.org/10.57702/td83wawx

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Author	Chun-Hsiao Yeh
More Authors	Cheng-Yao Hong Yen-Chi Hsu Tyng-Luh Liu Yubei Chen Yann LeCun
Homepage	https://arxiv.org/abs/2105.04906