Web: http://arxiv.org/abs/2110.05007

June 20, 2022, 1:13 a.m. | Xiaojun Jia, Yong Zhang, Baoyuan Wu, Jue Wang, Xiaochun Cao

cs.CV updates on arXiv.org arxiv.org

Adversarial training (AT) has been demonstrated to be effective in improving
model robustness by leveraging adversarial examples for training. However, most
AT methods are in face of expensive time and computational cost for calculating
gradients at multiple steps in generating adversarial examples. To boost
training efficiency, fast gradient sign method (FGSM) is adopted in fast AT
methods by calculating gradient only once. Unfortunately, the robustness is far
from satisfactory. One reason may arise from the initialization fashion.
Existing fast AT …

arxiv boosting cv training

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