1. Some conclusions are not convincing. For example, the paper contends that *We believe that continuous learning with unlabeled data accumulates noise, which is detrimental to representation quality.* The results might come from the limited exploration of combination methods. In rehearsal-free continual learning, feature-replay methods have shown great potential, like [R1] in continual learning and [R2] (FRoST) in CCD. A more recent work [R3] also employs feature replay to continually adjust the feature space, which also obtains remarkable performance for continual category discovery.
2. The proposed method is naïve and the novelty is relatively limited. The method includes basic clustering and number estimation. I’m afraid that this method could not provide so many insights to the community.
3. The feature space (i.e., backbone) is only tuned using labeled known classes, does this manner result in overfitting? because the data is purely labeled but the number is limited.
4. The class number estimation algorithm requires a pre-defined threshold $d_{min}$, which is intractable to define in advance and could largely impact the results. Some experiments and ablations should be included.
5. Detailed results of each continual session (at least for one or two datasets) should also be presented to show the performance. References:
[R1]. Prototype Augmentation and Self-Supervision for Incremental Learning. CVPR 2021.
[R2]. Class-incremental Novel Class Discovery. ECCV 2022.
[R3]. Happy: A Debiased Learning Framework for Continual Generalized Category Discovery. NeurIPS 2024. arXiv:2410.0653.
1. Some conclusions are not convincing. For example, the paper contends that *We believe that continuous learning with unlabeled data accumulates noise, which is detrimental to representation quality.* The results might come from the limited exploration of combination methods. In rehearsal-free continual learning, feature-replay methods have shown great potential, like [R1] in continual learning and [R2] (FRoST) in CCD. A more recent work [R3] also employs feature replay to continually adjust the feature space, which also obtains remarkable performance for continual category discovery.