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This content will become publicly available on June 27, 2024

Title: When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario, namely Single Domain Generalization (Single-DG), where only a single source domain is available for training. To tackle this challenge, we first try to understand when neural networks fail to generalize? We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as model sensitivity. Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. Models trained with these hard-to-learn samples can effectively suppress the sensitivity in the frequency space, which leads to improved generalization performance. Extensive experiments on multiple public datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 2.55%. The source code is available at https://github.com/DIAL-RPI/Spectral-Adversarial-Data-Augmentation.  more » « less
Award ID(s):
2046708
NSF-PAR ID:
10498679
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Association for the Advancement of Artificial Intelligence (AAAI)
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
37
Issue:
9
ISSN:
2159-5399
Page Range / eLocation ID:
11219 to 11227
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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