  Adversarial Training (AT) has been found to substantially improve the
robustness of deep learning classifiers against adversarial attacks. AT
involves obtaining robustness by including adversarial examples in training a
classifier. Most variants of AT algorithms treat every training example
equally. However, recent works have shown that better performance is achievable
by treating them unequally. In addition, it has been observed that AT exerts an
uneven influence on different classes in a training set and unfairly hurts
examples corresponding to classes that are inherently harder to classify.
Consequently, various reweighting schemes have been proposed that assign
unequal weights to robust losses of individual examples in a training set. In
this work, we propose a novel instance-wise reweighting scheme. It considers
the vulnerability of each natural example and the resulting information loss on
its adversarial counterpart occasioned by adversarial attacks. Through
extensive experiments, we show that our proposed method significantly improves
over existing reweighting schemes, especially against strong white and
black-box attacks.
