Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
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The paper introduces Selective Posterior Margin Regularization (SPMR), a technique that enhances Forward correction for learning with class‑conditional label noise. SPMR preserves the Forward objective while converting disagreements between the corrected likelihood’s reverse posterior and the observed annotation into a graded update on the clean classifier. Experiments on five known‑transition benchmarks show that SPMR improves performance by 2.5–7.0 percentage points over full‑length Forward and remains 0.7–2.5 percentage points better when combined with Mixup and early stopping, with gains attributed to posterior‑space coefficients, transition‑adjusted targets, and pairwise actions.
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