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Impact of Attention on Adversarial Robustness of Image Classification Models
[article]
2021
arXiv
pre-print
Adversarial attacks against deep learning models have gained significant attention and recent works have proposed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. Attention in computer vision has been used to incorporate focused learning of important features and has led to improved accuracy. Recently, models with attention mechanisms have been proposed to enhance adversarial robustness. Following this context, this work aims at a
arXiv:2109.00936v1
fatcat:6g27hqf2zfdgdctjsqqd6ju6d4