Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD

翻译：广义类别发现是一项务实且具有挑战性的开放世界任务，旨在利用少量旧类标记数据，将来自新类和旧类的未标记样本进行聚类。由于从旧类中学到的知识无法完全迁移到新类，且新类别完全未标注，广义类别发现本质上面临难以处理的问题，包括分类性能不平衡以及新旧类之间置信度不一致，尤其在低标注情形下。因此，对新类进行一定标注被认为是必要的。然而，标注新类别成本极高。为解决这一问题，我们借鉴主动学习的思想，提出一种名为主动广义类别发现的新设置。其目标是通过从标记者处主动选择有限数量的有价值样本进行标注，以提高广义类别发现的性能。为解决该问题，我们设计了一种自适应采样策略，该策略联合考虑新颖性、信息量和多样性，自适应地选择具有适当不确定性的新颖样本。然而，由于新类聚类导致的标签索引顺序变化，查询到的标签无法直接用于后续训练。为克服这一问题，我们进一步提出一种稳定的标签映射算法，该算法将真实标签转换到分类器的标签空间，从而确保在不同主动选择阶段训练的稳定性。我们的方法在通用和细粒度数据集上均达到了最优性能。我们的代码可在 https://github.com/mashijie1028/ActiveGCD 获取。