<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
<> <http://xmlns.com/foaf/0.1/primaryTopic> <https://discovery.ucl.ac.uk/id/eprint/10148476> .
<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/bibo/Article> .
<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://purl.org/dc/terms/title> "You Never Cluster Alone"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://purl.org/ontology/bibo/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 benchmarked datasets."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://purl.org/dc/terms/date> "2021-12-18" .
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<https://discovery.ucl.ac.uk/id/person/ext-798f073a33107a3628121a889739495c> <http://xmlns.com/foaf/0.1/givenName> "Ling"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-3c98e03151ce28c06fc4a5c919696fba> <http://xmlns.com/foaf/0.1/givenName> "Ziyi"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-4d76ef7c8ef46708b7034d2c8ea95ef7> <http://xmlns.com/foaf/0.1/name> "Philip Torr"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/bibo/Article> .
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<https://discovery.ucl.ac.uk/id/event/ext-9601837a9e256711d41e0b5504369e44> <http://purl.org/dc/terms/title> "35th Conference on Neural Information Processing Systems (NeurIPS 2021)."^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/eprint/10148476> <http://www.w3.org/2000/01/rdf-schema#seeAlso> <https://discovery.ucl.ac.uk/id/eprint/10148476/> .
<https://discovery.ucl.ac.uk/id/eprint/10148476/> <http://purl.org/dc/elements/1.1/title> "HTML Summary of #10148476 \n\nYou Never Cluster Alone\n\n" .
<https://discovery.ucl.ac.uk/id/eprint/10148476/> <http://purl.org/dc/elements/1.1/format> "text/html" .
<https://discovery.ucl.ac.uk/id/eprint/10148476/> <http://xmlns.com/foaf/0.1/primaryTopic> <https://discovery.ucl.ac.uk/id/eprint/10148476> .