Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data 事件

Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data arXiv:2605.31324v1 Announce Type: cross Abstract: Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theoretical understanding and practical applications. Leveraging unlabeled data for these purposes offers significant advantages in real-world scenarios. This paper introduces a novel generalization measure, local inconsisten