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Learning Weakly-Supervised Contrastive Representations
[article]
2022
arXiv
pre-print
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we present a two-stage weakly-supervised contrastive learning approach. The first stage is to cluster data according to its auxiliary information. The second stage is to learn similar
arXiv:2202.06670v2
fatcat:bvfndct2f5awbh37mtd2ylnlba