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Generalized category discovery (GCD) is a recently proposed open-worldproblem, which aims to automatically cluster partially labeled data. The mainchallenge is that the unlabeled data contain instances that are not only fromknown categories of the labeled data but also from novel categories. This leadstraditional novel category discovery (NCD) methods to be incapacitated for GCD,due to their assumption of unlabeled data are only from novel categories. Oneeffective way for GCD is applying self-supervised learning to learndiscriminate representation for unlabeled data. However, this manner largelyignores underlying relationships between instances of the same concepts (e.g.,class, super-class, and sub-class), which results in inferior representationlearning. In this paper, we propose a Dynamic Conceptional Contrastive Learning(DCCL) framework, which can effectively improve clustering accuracy byalternately estimating underlying visual conceptions and learning conceptionalrepresentation. In addition, we design a dynamic conception generation andupdate mechanism, which is able to ensure consistent conception learning andthus further facilitate the optimization of DCCL. Extensive experiments showthat DCCL achieves new state-of-the-art performances on six generic andfine-grained visual recognition datasets, especially on fine-grained ones. Forexample, our method significantly surpasses the best competitor by 16.2% on thenew classes for the CUB-200 dataset. Code is available athttps://github.com/TPCD/DCCL.

Dynamic Conceptional Contrastive Learning for Generalized Category Discovery