Maximum Spanning Tree Guided Confidence and Sparse Graph for Robust Noisy Label Learning

Abstract

DNNs are highly susceptible to noisy labels, often leadingto unstable training and severe performance degradation. Graph-basedmethods have emerged as a promising solution, utilizing sample rela-tionships to refine labels through neighborhood aggregation. However,most existing approaches rely on dense connectivity (e.g., KNN graphs),which tends to aggregate noisy cues and cause oversmoothing. Whilesparse graphs offer a potential remedy, current sparse topologies oftensuffer from blind error propagation between samples, particularly forhard samples near class boundaries. To address these challenges, we pro-pose Maximum Spanning Tree (MST) Guided Confidence and SparseGraph Network (MST-GCSN). We introduce the MST as a robust sparsebackbone to filter out redundant local noise while preserving a globalmanifold skeleton. To rectify the indiscriminate mutual influence betweennodes, a Confidence-Gated Propagation (CGP) mechanism is designedto adaptively regulate information flow based on node reliability, ensur-ing that only high-confidence semantic signals are propagated. Buildingon this optimized structure, a Progressive Label Prediction (PLP) mod-ule integrates local information and global anchor-guided cues to itera-tively correct labels. Extensive experiments on synthetic and real-worldnoisy datasets demonstrate that MST-GCSN significantly enhances ro-bustness.