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Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discovery
Despite their progress, our analysis experiments show that novel classes can achieve impressive clustering results on the feature space of a known class pre-trained model, suggesting that existing methods may not fully utilize known class knowledge.
Class-relation Knowledge Distillation for Novel Class Discovery
In addition, to enable a flexible knowledge distillation scheme for each data point in novel classes, we develop a learnable weighting function for the regularization, which adaptively promotes knowledge transfer based on the semantic similarity between the novel and known classes.
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