ReCL: A Plug-and-Play Module for Enhancing Generalized Category Discovery Using Transport-Based Method to Uncover the...

Deep learning systems excel in closed-set environments but face challenges in open-set settings due to mismatched label spaces between training and test data. Generalized category discovery (GCD) is one of such real-world open-set learning problems. In GCD, given a dataset, only a subset of samples is labeled. The model is expected to simultaneously classify samples from labeled and unlabeled classes. Contrastive learning plays a critical role in solving the GCD problem, used to learn discriminative features for samples. However, in contrast to labeled data, due to the absence of label information, unlabeled samples rely solely on unsupervised contrastive loss to learn discriminated features by keeping different views of the same data consistent. Unfortunately, this approach often overlooks the relationships within unlabeled samples. In this article, we propose a relationship-based contrastive learning (ReCL) module. In ReCL, we use a transport-based assignment method to find appropriate samples for each unlabeled data point. Then, a prototype-based fusion method is applied to merge these selected samples, creating a positive anchor in contrastive learning that helps pull the unlabeled samples closer to the corresponding positive anchor. Extensive experimental evaluation across different domains demonstrates that our method can be seamlessly integrated with various existing GCD models and further improve them to achieve the state-of-the-art performance across different benchmarks. Notably, we also analyze the sample selection process between our transport-based method and the cosine similarity-based method. The results show that our method provides samples that contain semantic similarity while offering greater diversity.