  Generalized class discovery (GCD) aims to infer known and unknown categories
in an unlabeled dataset leveraging prior knowledge of a labeled set comprising
known classes. Existing research implicitly/explicitly assumes that the
frequency of occurrence for each category, whether known or unknown, is
approximately the same in the unlabeled data. However, in nature, we are more
likely to encounter known/common classes than unknown/uncommon ones, according
to the long-tailed property of visual classes. Therefore, we present a
challenging and practical problem, Imbalanced Generalized Category Discovery
(ImbaGCD), where the distribution of unlabeled data is imbalanced, with known
classes being more frequent than unknown ones. To address these issues, we
propose ImbaGCD, A novel optimal transport-based expectation maximization
framework that accomplishes generalized category discovery by aligning the
marginal class prior distribution. ImbaGCD also incorporates a systematic
mechanism for estimating the imbalanced class prior distribution under the GCD
setup. Our comprehensive experiments reveal that ImbaGCD surpasses previous
state-of-the-art GCD methods by achieving an improvement of approximately 2 -
4% on CIFAR-100 and 15 - 19% on ImageNet-100, indicating its superior
effectiveness in solving the Imbalanced GCD problem.
