  Adversarial training (AT) is a popular method for training robust deep neural
networks (DNNs) against adversarial attacks. Yet, AT suffers from two
shortcomings: (i) the robustness of DNNs trained by AT is highly intertwined
with the size of the DNNs, posing challenges in achieving robustness in smaller
models; and (ii) the adversarial samples employed during the AT process exhibit
poor generalization, leaving DNNs vulnerable to unforeseen attack types. To
address these dual challenges, this paper introduces adversarial training via
adaptive knowledge amalgamation of an ensemble of teachers (AT-AKA). In
particular, we generate a diverse set of adversarial samples as the inputs to
an ensemble of teachers; and then, we adaptively amalgamate the logtis of these
teachers to train a generalized-robust student. Through comprehensive
experiments, we illustrate the superior efficacy of AT-AKA over existing AT
methods and adversarial robustness distillation techniques against cutting-edge
attacks, including AutoAttack.
