Reliable Adversarial Distillation with Unreliable Teachers
This addresses the challenge of improving adversarial robustness in neural networks through distillation, but it is incremental as it builds on existing adversarial distillation methods.
The paper tackles the problem of adversarial distillation where teachers may be unreliable for adversarial data, proposing introspective adversarial distillation (IAD) that allows students to partially trust teachers based on their performance, resulting in improved adversarial robustness over teachers.
In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are stronger supervision than the original hard labels. However, when considering adversarial robustness, teachers may become unreliable and adversarial distillation may not work: teachers are pretrained on their own adversarial data, and it is too demanding to require that teachers are also good at every adversarial data queried by students. Therefore, in this paper, we propose reliable introspective adversarial distillation (IAD) where students partially instead of fully trust their teachers. Specifically, IAD distinguishes between three cases given a query of a natural data (ND) and the corresponding adversarial data (AD): (a) if a teacher is good at AD, its SL is fully trusted; (b) if a teacher is good at ND but not AD, its SL is partially trusted and the student also takes its own SL into account; (c) otherwise, the student only relies on its own SL. Experiments demonstrate the effectiveness of IAD for improving upon teachers in terms of adversarial robustness.