Happy++: Towards Stable and Unified Continual Generalized Category Discovery.

Abstract

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data without forgetting old classes. In this work, we study a more practical setting, which includes more new classes over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data, followed by multiple incremental stages that incorporate both old and new classes in unlabeled data. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We identify two types of bias to which models are susceptible: prediction bias, where the model tends to predict new classes as old ones, and hardness bias, where forgetting on some particularly difficult old classes is exceptionally severe. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, significantly enhancing their clustering performance. For hardness bias, we present hardness-aware prototype sampling, which effectively reduces the forgetting of previous classes. Building upon Happy, we further propose Happy++ by incorporating three training strategies, achieving more stable and generalizable performance. We further broaden the task scope by extending the model to detect out-of-distribution samples, which are commonly present in practical scenarios, thereby serving as a unified open-world classifier, called Happy++O. Experimental results demonstrate that our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various settings.