LiLAW: Lightweight Learnable Adaptive Weighting to Meta-Learn Sample Difficulty, Improve Noisy Training, Increase Fairness, and Effectively Use Synthetic Data

Abstract

Training deep neural networks with noise and data heterogeneity is a major challenge. We introduce Lightweight Learnable Adaptive Weighting (LiLAW), a method that dynamically adjusts the loss weight of each training sample based on its evolving difficulty, categorized as easy, moderate, or hard. Using only three learnable parameters, LiLAW adaptively prioritizes informative samples during training by updating these parameters using a single gradient descent step on a validation mini-batch after each training mini-batch. Experiments across multiple general and medical imaging datasets, noise levels/types, loss functions, and architectures with and without pretraining (with linear probing and full fine-tuning) demonstrate that LiLAW's effectiveness, even in high-noise environments, without excessive tuning. We also apply LiLAW to two recently introduced synthetic datasets: SynPAIN (synthetic facial expressions for automated pain detection) and GAITGen (synthetic gait sequences for Parkinson's disease severity estimation). We also validate on ECG5000, a time-series dataset for heartbeat classification, with simple augmentations. We obtain state-of-the-art results on these three datasets. We then use LiLAW on the Adult dataset to show improved fairness. LiLAW is effective without heavy reliance on advanced training techniques or data augmentations, highlighting its practicality, esp. in resource-constrained settings. It offers a computationally efficient solution to boost generalization and robustness in any neural network training setup.

Reproductions