MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

• URL: http://arxiv.org/abs/2111.05073v1
• Date: Tue, 9 Nov 2021 12:03:20 GMT
• Title: MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
• Authors: Muhammad Awais and Fengwei Zhou and Chuanlong Xie and Jiawei Li and Sung-Ho Bae and Zhenguo Li
• Abstract summary: Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. adversarial training constructs adversarial examples during training by iterative generalization of loss. This min-max generalization requires more data, larger capacity models, and additional computing resources. We show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
• Score: 24.22149102286949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is adversarial training which constructs adversarial examples during training by iterative maximization of loss. The model is then trained to minimize the loss on these constructed examples. This min-max optimization requires more data, larger capacity models, and additional computing resources. It also degrades the standard generalization performance of a model. Can we achieve robustness more efficiently? In this work, we explore this question from the perspective of knowledge transfer. First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation. Second, we propose a novel robustness transfer method called Mixup-Based Activated Channel Maps (MixACM) Transfer. MixACM transfers robustness from a robust teacher to a student by matching activated channel maps generated without expensive adversarial perturbations. Finally, extensive experiments on multiple datasets and different learning scenarios show our method can transfer robustness while also improving generalization on natural images.

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