Learning to Distinguish Samples for Generalized Category Discovery

Generalized Category Discovery (GCD) utilizes labelled data from seen categories to cluster unlabelled samples from both seen and unseen categories. Previous methods have demonstrated that assigning pseudo-labels for representation learning is effective. However, these methods commonly predict pseudo-labels based on pairwise similarities, while the overall relationship among each instance’s k-nearest neighbors (kNNs) is largely overlooked, leading to inaccurate pseudo-labeling. To address this issue, we introduce a Neighbor Graph Convolutional Network (NGCN) that learns to predict pairwise similarities between instances using only labelled data. NGCN explicitly leverages the relationships among each instance’s kNNs and is generalizable to samples of both seen and unseen classes. This helps produce more accurate positive samples by injecting the predicted similarities into subsequent clustering. Furthermore, we design a Cross-View Consistency Strategy (CVCS) to exclude samples with nois...