Soft Cluster-Aware Equivariant Contrastive Learning for Unsupervised Out-of-Distribution Detection
Kuiyun Huang, Menglong Chen, Hong Yu Zheng, Baihong Lin, Shicai Fan · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Recent works try to combine clustering and contrastive learning for unsupervised out-of-distribution (OOD) detection, since these two schemes can exploit semantic information and bring in discriminative representation learning. However, most methods based on clustering and contrastive learning struggle with the problems of hard assignment and low-level clustering, i.e., they usually assign each sample to one single cluster and obtain clusters of similar low-level features, which can easily bring in numerous incorrect assignments and hinder the learning of semantic information. To address these problems, this paper proposes a novel framework for unsupervised OOD detection named Soft Cluster-aware Equivariant Contrastive Learning (SCECL). Different from previous works, SCECL devises two modules named Soft Cluster-aware Semantic Relationship Mining (SCSRM) and Contrastive Learning with Invariance and Equivariance (CLIE): SCSRM assigns each sample to multiple clusters with soft assignment weights and utilizes the soft assignment weights with semantic relationships to guide unsupervised contrastive learning for OOD detection, while CLIE introduces the equivariance principle as an additional inductive bias, encouraging the model to learn more discriminative semantic features to avoid low-level clustering. Extensive experimental results on various OOD detection benchmarks demonstrate that the proposed SCECL can effectively utilize semantic information for discriminative representation learning and achieve state-of-the-art performance.