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Readiness-Aware Sample Selection for Noisy Labels with Class Imbalance

NeurIPS 2026 · Label-efficient and robust learning › Noisy labels · approach: Sample selection and co-training

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Proposes a readiness-aware sample selection method for training with noisy labels under class imbalance, addressing the failure of fixed warm-up periods when majority classes dominate early learning.

Problem

Sample selection for noisy-label learning relies on a warm-up phase assuming the model learns class-specific patterns early; under class imbalance, majority classes hinder learning of minority classes, so this assumption often fails.

Approach

Selects clean samples based on a per-model readiness signal rather than a fixed warm-up duration, so selection begins only once the model has learned class-specific patterns (details not available beyond the abstract).

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