AINeutralarXiv – CS AI · 7h ago6/10
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Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data
Researchers introduce Inconsistency-Aware Minimization (IAM), a novel training method that leverages unlabeled data to improve neural network generalization by measuring local inconsistency in parameter space. The approach matches or exceeds existing methods like Sharpness-Aware Minimization while offering advantages in semi- and self-supervised learning scenarios.