Title: Achieving Noise Robustness by additive normalization of labels
Primary area: learning theory
Keywords: Noise robustness, NRFL, WRLL

Abstract: As machine learning models scale, the demand for large volumes of high-quality training data grows, but acquiring clean datasets is costly and time-consuming due to detailed human annotation and noisy data filtering challenges. To address this, symmetric loss functions were introduced in the context of label noise, enabling models trained on noisy data to perform comparably to those trained on clean data without explicit noise knowledge. Loss functions satisfying a specific symmetry condition exhibit robustness to label noise. Building on this, we propose a novel method to derive noise-robust loss functions using monotonic functions and label normalisation, which involves a simple normalisation of labels that leads to noise robustness when labels are corrupted. Unlike other approaches, this method allows creation of new loss functions by defining application-specific monotonic functions rather than relying on predefined losses. We formally prove their theoretical properties, propose two concrete noise-robust losses, and demonstrate through extensive empirical evaluations on computer vision and natural language processing tasks that our losses outperform standard and existing noise-robust losses. Our evaluations indicate better learning of decision boundaries, faster convergence, and improved robustness to noise using the proposed loss functions.