Inconsistency-Aware Minimization (IAM

Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data

Announce Type: new 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 inconsistency, derived from an information-geometric perspective on the parameter space of neural networks.