  Generalized category discovery (GCD) is a recently proposed open-world
problem, which aims to automatically cluster partially labeled data. The main
challenge is that the unlabeled data contain instances that are not only from
known categories of the labeled data but also from novel categories. This leads
traditional novel category discovery (NCD) methods to be incapacitated for GCD,
due to their assumption of unlabeled data are only from novel categories. One
effective way for GCD is applying self-supervised learning to learn
discriminate representation for unlabeled data. However, this manner largely
ignores underlying relationships between instances of the same concepts (e.g.,
class, super-class, and sub-class), which results in inferior representation
learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning
(DCCL) framework, which can effectively improve clustering accuracy by
alternately estimating underlying visual conceptions and learning conceptional
representation. In addition, we design a dynamic conception generation and
update mechanism, which is able to ensure consistent conception learning and
thus further facilitate the optimization of DCCL. Extensive experiments show
that DCCL achieves new state-of-the-art performances on six generic and
fine-grained visual recognition datasets, especially on fine-grained ones. For
example, our method significantly surpasses the best competitor by 16.2% on the
new classes for the CUB-200 dataset. Code is available at
https://github.com/TPCD/DCCL.
