LiLAW: Lightweight Learnable Adaptive Weighting from Confidence and Disagreement

High-loss training examples may be mislabeled or simply difficult to classify, making loss alone an unreliable guide to sample weighting under label noise. We introduce Lightweight Learnable Adaptive Weighting (LiLAW), which assigns sample weights using two values: the model’s confidence in the observed label and its confidence in its preferred prediction. Three global parameters control these weights and are learned by evaluating the effect of a weighted classifier update on a held-out batch, without requiring clean labels. We characterize the resulting weights and identify the conditions under which the ratio of the two probabilities distinguishes correct and incorrect annotations. Across CIFAR-10N and CIFAR-100N, LiLAW improves test accuracy in nearly all comparisons involving four frozen pretrained encoders. We also examine selective classification: abstaining on less confident predictions yields higher accuracy at lower coverage. With full-network training on Clothing1M, LiLAW achieves consistently improved results across multiple seeds, while being very lightweight. These findings suggest that a small set of learned weighting parameters can improve learning from real-world label noise and support selective classification.