High-Quality Label Learning in Generalized Category Discovery

Generalized Category Discovery (GCD) is a recently proposed open-world problem that aims to automatically classify and discover new categories based on partially labeled data. For unlabeled data, previous research commonly considers using pseudo-labels to assist in model learning. These pseudo-labels, together with the true labels of labeled data, form the learning targets for the final classifier, leading to better predictive outcomes. However, low-quality labels can inevitably hinder the learning process of the model. To address this issue, inspired by previous methods, we propose the Calibrated Generalized Category Discovery (CGCD) framework, which incorporates a projection head, a classifier head, and a calibration head. The projection head is used for representation learning. The calibration head learns high-quality labels from the robust predictions of the classifier head, and the classifier head utilizes these high-quality labels for more efficient learning. Both heads mutually enhance each other during training, ultimately leading to a superior solution. In addition, leveraging the characteristics of both the classifier head and calibration head, we designed a classifier representation distribution regularization term to further ensure consistency in their learning processes. Extensive experimental results demonstrate that the proposed CGCD framework achieves state-of-the-art performance across five general and fine-grained visual recognition datasets by leveraging high-quality label learning.