Augmentation-invariant Learning Strategy via Data Augmentation for Improving Model Generalization

Yu Miao; Juanjuan Zhao; Sijie Song; Ran Gong; Yuanqian Zhu; Lusha Qi; Yan Qiang

AAAI 2026technical0 citations

Augmentation-invariant Learning Strategy via Data Augmentation for Improving Model Generalization

Yu Miao, Juanjuan Zhao, Sijie Song, Ran Gong, Yuanqian Zhu, Lusha Qi, Yan Qiang

Abstract

Data augmentation is an effective technique for regularizing deep networks, which helps to enhance the generalizability and robustness of the model. However, in the field of medical imaging, traditional data augmentation techniques such as cropping, rotation, and degradation may inadvertently alter the critical characteristics of pathological lesions. Conventional semantic augmentation methods, such as altering the color and contrast of the object background, may also affect the structural features of medical images in uncontrolled semantic directions. Such operational conditions compromise the model

BibTeX

@inproceedings{aaai2026_augmentationinva,
  title = {Augmentation-invariant Learning Strategy via Data Augmentation for Improving Model Generalization},
  author = {Yu Miao and Juanjuan Zhao and Sijie Song and Ran Gong and Yuanqian Zhu and Lusha Qi and Yan Qiang},
  booktitle = {AAAI 2026},
  year = {2026}
}