Web: http://arxiv.org/abs/2106.01908

Jan. 24, 2022, 2:11 a.m. | Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip H.S. Torr, Ling Shao

cs.LG updates on arXiv.org arxiv.org

Recent advances in self-supervised learning with instance-level contrastive
objectives facilitate unsupervised clustering. However, a standalone datum is
not perceiving the context of the holistic cluster, and may undergo sub-optimal
assignment. In this paper, we extend the mainstream contrastive learning
paradigm to a cluster-level scheme, where all the data subjected to the same
cluster contribute to a unified representation that encodes the context of each
data group. Contrastive learning with this representation then rewards the
assignment of each datum. To implement …

arxiv cv

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