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• W3169233287 abstract "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 this vision, we propose twin-contrast clustering (TCC). We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster. We further propose heuristic cluster augmentation equivalents to enable cluster-level contrastive learning. On the other hand, we derive the evidence lower-bound of the instance-level contrastive objective with the assignments. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps. Extensive experiments show that TCC outperforms the state-of-the-art on challenging benchmarks." @default.
• W3169233287 created "2021-06-22" @default.
• W3169233287 creator A5009086593 @default.
• W3169233287 creator A5040995630 @default.
• W3169233287 creator A5042899882 @default.
• W3169233287 creator A5052917688 @default.
• W3169233287 creator A5061215231 @default.
• W3169233287 creator A5082634513 @default.
• W3169233287 date "2021-06-03" @default.
• W3169233287 modified "2023-10-16" @default.
• W3169233287 title "You Never Cluster Alone" @default.
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