  Standard adversarial training approaches suffer from robust overfitting where
the robust accuracy decreases when models are adversarially trained for too
long. The origin of this problem is still unclear and conflicting explanations
have been reported, i.e., memorization effects induced by large loss data or
because of small loss data and growing differences in loss distribution of
training samples as the adversarial training progresses. Consequently, several
mitigation approaches including early stopping, temporal ensembling and weight
perturbations on small loss data have been proposed to mitigate the effect of
robust overfitting. However, a side effect of these strategies is a larger
reduction in clean accuracy compared to standard adversarial training. In this
paper, we investigate if these mitigation approaches are complimentary to each
other in improving adversarial training performance. We further propose the use
of helper adversarial examples that can be obtained with minimal cost in the
adversarial example generation, and show how they increase the clean accuracy
in the existing approaches without compromising the robust accuracy.
