AdvMixUp: Adversarial MixUp Regularization for Deep Learning.
basic_science · Level V
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- Record sourced from PubMed, PMID 40327482.
- Also identified by DOI 10.1109/TNNLS.2025.3562363.
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Abstract
Deep neural networks (DNNs) have shown significant progress in many application fields. However, overfitting remains a significant challenge in their development. While existing data-augmentation techniques such as MixUp have been successful in preventing overfitting, they often fail to generate hard mixed samples near the decision boundary, impeding model optimization. In this article, we present adversarial MixUp (AdvMixUp), a novel sample-dependent method for regularizing DNNs. AdvMixUp addresses this issue by incorporating adversarial training (AT) to create sample-dependent and feature-level interpolation masks, generating more challenging mixed samples. These virtual samples enable DNNs to learn more robust features, ultimately reducing overfitting. Empirical evaluations on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet demonstrate that AdvMixUp outperforms existing MixUp variants.