  Previous adversarial training raises model robustness under the compromise of
accuracy on natural data. In this paper, we reduce natural accuracy
degradation. We use the model logits from one clean model to guide learning of
another one robust model, taking into consideration that logits from the well
trained clean model embed the most discriminative features of natural data,
{\it e.g.}, generalizable classifier boundary. Our solution is to constrain
logits from the robust model that takes adversarial examples as input and makes
it similar to those from the clean model fed with corresponding natural data.
It lets the robust model inherit the classifier boundary of the clean model.
Moreover, we observe such boundary guidance can not only preserve high natural
accuracy but also benefit model robustness, which gives new insights and
facilitates progress for the adversarial community. Finally, extensive
experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet testify to the
effectiveness of our method. We achieve new state-of-the-art robustness on
CIFAR-100 without additional real or synthetic data with auto-attack benchmark
\footnote{\url{https://github.com/fra31/auto-attack}}. Our code is available at
\url{https://github.com/dvlab-research/LBGAT}.
