Early Stopping Against Label Noise Without Validation Data
To address the performance degradation and model selection bias caused by validation-set-dependent early stopping under label noise, this paper proposes Label Wave—a validation-free, noise-robust early stopping method. Label Wave monitors the dynamic fluctuations of per-sample prediction confidence on the training set, revealing a strong correlation between minima in these fluctuations and the optimal generalization point. It automatically terminates training via sliding-window quantification and adaptive peak detection. As the first fully validation-free, noise-aware early stopping framework, it establishes a novel link between training dynamics (specifically, confidence fluctuation patterns) and generalization capability. Extensive experiments on benchmarks including CIFAR-10/100 and WebVision demonstrate that Label Wave consistently improves the accuracy of state-of-the-art noisy-label learning methods by 3.2–5.7% on average, while exhibiting superior stability compared to conventional validation-based early stopping.