SA-MDRAD: sample-adaptive multi-teacher dynamic rectification adversarial distillation

Adversarial training of lightweight models faces poor effectiveness problem due to the limited model size and the difficult optimization of loss with hard labels. Adversarial distillation is a potential solution to the problem, in which the knowledge from large adversarially pre-trained teachers is used to guide the lightweight models’ learning. However, adversarially pre-training teachers is computationally expensive due to the need for iterative gradient steps concerning the inputs. Additionally, the reliability of guidance from teachers diminishes as lightweight models become more robust. In this paper, we propose an adversarial distillation method called Sample-Adaptive Multi-teacher Dynamic Rectification Adversarial Distillation (SA-MDRAD). First, an adversarial distillation framework of distilling logits and features from the heterogeneous standard pre-trained teachers is developed to reduce pre-training expenses and improve knowledge diversity. Second, the knowledge of teachers is distilled into the lightweight model after sample-aware dynamic rectification and adaptive fusion based on teachers’ predictions to improve the reliability of knowledge. Experiments are conducted to evaluate the performance of the proposed method on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets. The results demonstrate that our SA-MDRAD is more effective than existing adversarial distillation methods in enhancing the robustness of lightweight image classification models against various adversarial attacks.