Otem-IGCD: An Optimal Transport-based EM Framework for Imbalanced Generalized Category Discovery

Generalized Class Discovery (GCD) seeks to identify both known and unknown categories within an unlabeled dataset, utilizing the knowledge from a labeled dataset of 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, real-world scenarios often exhibit a long-tailed distribution of visual classes, where known or common classes appear more frequently than unknown or rare ones. Addressing this discrepancy, we introduce a new challenge: Imbalanced Generalized Category Discovery (IGCD), which deals with an imbalanced distribution in unlabeled data, favoring known over unknown classes. To tackle this, we propose a novel Optimal Transport-based Expectation Maximization framework for Imbalanced Generalized Category Discovery (Otem-IGCD) by aligning the marginal class prior distribution. Otem-IGCD also incorporates a systematic mechanism for estimating the imbalanced class prior distribution under the GCD setup. Our comprehensive experiments reveal that Otem-IGCD surpasses previous state-of-the-art GCD methods by achieving an improvement of approximately 2 - 4% on CIFAR100 and 15 - 19% on ImageNet-100, indicating its superior effectiveness in solving the Imbalanced GCD problem.