arXiv:2606.16050v1 Announce Type: cross Abstract: Robust deep learning under heavy-tailed and impulsive noise remains challenging because conventional losses such as mean squared error (MSE) exhibit unbounded sensitivity to outliers. Although correntropy-based objectives improve robustness, existing formulations rely on fixed kernel parameters that must be empirically tuned and remain static during training. To address these limitations, we propose an Adaptive Log-Correntropy Loss (ALCL), a heavy-tailed loss formulation that adaptively learns its robustness geometry during optimization. ALCL i
The continuous drive for more robust and reliable AI systems, especially in scenarios with imperfect data, necessitates innovations like adaptive loss functions.
This research contributes to making AI models more resilient to real-world noise and outliers, enhancing their deployability and trustworthiness in critical applications.
Deep learning models can now be trained with an adaptively configured loss function, potentially reducing the empirical tuning burden and improving performance in adverse conditions.
- · AI developers
- · Robust AI systems
- · Industries with noisy data
- · AI models sensitive to outliers
- · Manual hyperparameter tuners
Improved performance and reliability of AI models in real-world, noisy environments.
Accelerated adoption of deep learning in fields previously hampered by data quality issues.
Reduced computational overhead and expertise required for deploying robust AI solutions, democratizing access.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI