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Adaptive Log-Correntropy Loss Boosts Deep Learning Under Heavy-Tailed Noise Technology
Artificial Intelligence #machine learning#loss function

Adaptive Log-Correntropy Loss Boosts Deep Learning Under Heavy-Tailed Noise

Researchers propose ALCL, an adaptive log-correntropy loss function that learns its robustness parameters during training, outperforming standard losses in high-noise image benchmarks. The method improves median accuracy by up to 4.75% on grayscale and 4.51% on RGB datasets under heavy-tailed and impulsive noise.

Jun 17, 2026 1 source