Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general. Our code and data are available at https://github.com/Lackel/TAN.

翻译：广义类别发现是一项重要的现实任务。尽管在已知类别上性能有所提升，但现有方法在新颖类别上表现不佳。我们将这一性能不足归因于两个原因：有标签数据与无标签数据之间存在有偏的知识迁移，以及无标签数据上存在噪声表示学习。为缓解这两个问题，我们提出一种迁移与对齐网络（TAN），该网络融合了两种知识迁移机制以校准有偏知识，以及两种特征对齐机制以学习判别性特征。具体而言，我们通过原型建模不同类别，并迁移有标签数据中的原型以修正模型对已知类别的偏差。一方面，我们将无标签数据中已知类别的实例拉近至这些原型，形成更紧凑的聚类，避免已知类别与新颖类别间的边界重叠。另一方面，我们基于类别相似性，利用这些原型校准从无标签数据中估计出的噪声原型，从而更准确地估计新颖类别的原型，这些原型随后可作为可靠的学习目标。知识迁移后，我们进一步提出两种特征对齐机制，通过将实例特征与增强特征及校准后的原型对齐，从无标签数据中获取实例级和类别级知识，从而在降低噪声的同时提升模型在已知和新颖类别上的性能。在三个基准数据集上的实验表明，我们的模型优于现有最先进方法，尤其是在新颖类别上。我们提供了理论分析以深入理解模型的一般性行为。代码与数据见 https://github.com/Lackel/TAN。