Relational Pattern Consistency improves generalized category discovery by using invariant relational patterns between novel samples and known-class prototypes for bidirectional knowledge transfer.
Interlude: In- teractions between labeled and unlabeled data to enhance semi- supervised learning,
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DeCon decouples LTSSL into head-class and tail-class branches that interact and converge, delivering SOTA accuracy on mismatched-distribution benchmarks and outperforming prior methods even on matched distributions.
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Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery
Relational Pattern Consistency improves generalized category discovery by using invariant relational patterns between novel samples and known-class prototypes for bidirectional knowledge transfer.
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Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning
DeCon decouples LTSSL into head-class and tail-class branches that interact and converge, delivering SOTA accuracy on mismatched-distribution benchmarks and outperforming prior methods even on matched distributions.