This paper addresses the problem of Rehearsal-Free Continual Category Discovery (RF-CCD), which focuses on continuously identifying novel class by leveraging knowledge from labeled data. Existing methods typically train from scratch, overlooking the potential of base models, and often resort to data storage to prevent forgetting. Moreover, because RF-CCD encompasses both continual learning and novel class discovery, previous approaches have struggled to effectively integrate advanced techniques from these fields, resulting in less convincing comparisons and failing to reveal the unique challenges posed by RF-CCD. To address these challenges, we lead the way in integrating advancements from both domains and conducting extensive experiments and analyses. Our findings demonstrate that this integration can achieve state-of-the-art results, leading to the conclusion that in the presence of pre-trained models, the representation does not improve and may even degrade with the introduction of unlabeled data. To mitigate representation degradation, we propose a straightforward yet highly effective baseline method. This method first utilizes prior knowledge of known categories to estimate the number of novel classes. It then acquires representations using a model specifically trained on the base classes, generates high-quality pseudo-labels through k-means clustering, and trains only the classifier layer. We validate our conclusions and methods by conducting extensive experiments across multiple benchmarks, including the Stanford Cars, CUB, iNat, and Tiny-ImageNet datasets. The results clearly illustrate our findings, demonstrate the effectiveness of our baseline, and pave the way for future advancements in RF-CCD.

本文针对无重演持续类别发现（RF-CCD）问题，提出了一种新的方法来整合已有知识以识别新的类别。研究表明，利用预训练模型可以有效提高效果，而我们提出的基准方法通过聚类生成高质量的伪标签，从而实现了在多个数据集上的最佳性能，为未来研究指明了方向。

冻结与聚类：无重演持续类别发现的简单基准