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Delving into Inter-Image Invariance for Unsupervised Visual Representations. (arXiv:2008.11702v3 [cs.CV] UPDATED)
Sept. 16, 2022, 1:12 a.m. | Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
cs.LG updates on arXiv.org arxiv.org
Contrastive learning has recently shown immense potential in unsupervised
visual representation learning. Existing studies in this track mainly focus on
intra-image invariance learning. The learning typically uses rich intra-image
transformations to construct positive pairs and then maximizes agreement using
a contrastive loss. The merits of inter-image invariance, conversely, remain
much less explored. One major obstacle to exploit inter-image invariance is
that it is unclear how to reliably construct inter-image positive pairs, and
further derive effective supervision from them since no …
More from arxiv.org / cs.LG updates on arXiv.org
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