标题： 通过互学习和类分布正则化的广义类别发现 显示英文标题

标题： Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization

摘要： 广义类别发现（GCD）旨在通过利用有标注样本的基础知识来识别未标注样本，其中未标注集合包括基础类和新颖类。 由于聚类方法在推理时耗时较长，基于参数的方法变得更加流行。 然而，近期基于参数的方法由于自监督的不可靠性，在基础区分方面表现较差。 为了解决这个问题，我们提出了一种互学习框架（RLF），该框架引入了一个专注于基础分类的辅助分支。 在训练过程中，主分支将伪基础样本过滤到辅助分支。 作为响应，辅助分支为主分支提供了更可靠的软标签，从而形成良性循环。 此外，我们引入了类分布正则化（CDR），以减轻对基础类别的学习偏差。 CDR本质上提高了未标注数据的预测置信度，并提升了新颖类别的性能。 结合这两个组件，我们提出的方法RLCD在所有类别上都取得了卓越的性能，且额外计算量可以忽略不计。 在七个GCD数据集上的综合实验验证了其优越性。 我们的代码可在https://github.com/APORduo/RLCD获取。 显示英文摘要

摘要： Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unlabeled set consists of both base and novel classes. Since clustering methods are time-consuming at inference, parametric-based approaches have become more popular. However, recent parametric-based methods suffer from inferior base discrimination due to unreliable self-supervision. To address this issue, we propose a Reciprocal Learning Framework (RLF) that introduces an auxiliary branch devoted to base classification. During training, the main branch filters the pseudo-base samples to the auxiliary branch. In response, the auxiliary branch provides more reliable soft labels for the main branch, leading to a virtuous cycle. Furthermore, we introduce Class-wise Distribution Regularization (CDR) to mitigate the learning bias towards base classes. CDR essentially increases the prediction confidence of the unlabeled data and boosts the novel class performance. Combined with both components, our proposed method, RLCD, achieves superior performance in all classes with negligible extra computation. Comprehensive experiments across seven GCD datasets validate its superiority. Our codes are available at https://github.com/APORduo/RLCD.