Readiness-Aware Sample Selection for Noisy Labels with Class Imbalance

Noisy labels can cause deep neural networks to overfit incorrect annotations, leading to biased predictions and poor generalization. A common remedy is to select clean samples after a short warm-up phase, under the assumption that the model begins to learn class-specific patterns from the early training stage. However, this assumption often fails under class imbalance, as the majority classes can hinder the model from learning distinctive features of minority classes. In this paper, we propose a readiness-aware sample selection strategy that identifies clean samples only from classes whose distinctive features have been sufficiently learned and are thus considered ready for selection. We further introduce a novel negative learning scheme to enhance class separability by discouraging confusion with the most similar incorrect classes. The proposed method is supported by theoretical analysis and demonstrates outstanding performance on both benchmark and real-world datasets, showing improved robustness and generalization under noisy and class-imbalanced conditions.