  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
