Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data

ArXi:2605.31324v1 Announce Type: cross 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