Sandbox: Safeguarded Multi-Label Learning Through Safe Optimal Transport

Abstract

Multi-label learning with label noise presents significant real-world challenges due to dependencies among labels, complicating the transition from clean to noisy labels. Mainstream approaches, such as robust loss functions, and noise transition models, often fall short due to their high sensitivity to noise rate or transition matrix estimation, especially on complex datasets. To address these challenges, we introduce a novel Sandbox mechanism, which iteratively estimates multiple labels. Unlike traditional methods that depend on an explicit, often restrictive linear transition matrix, Sandbox mechanism utilizes an implicit optimal transport process, constraining label refinements between noisy labels and model predictions within a predefined polytope, effectively limiting error propagation, enhancing stability, and offering greater flexibility. In each iteration, we develop a simple yet effective method, termed as Safe Optimal Transport (SOT), to refine noisy labels more reliably towards the ground truth. By involving the interpolated references and complementary orientations, SOT effectively estimates true labels using the Sinkhorn-Knopp algorithm. Our extensive evaluations on various benchmark datasets demonstrate that Sandbox consistently outperforms existing state-of-the-art techniques. Comprehensive ablation studies further elucidate its effectiveness.