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- W3213231502 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." @default.
- W3213231502 created "2021-11-22" @default.
- W3213231502 creator A5036381691 @default.
- W3213231502 creator A5036948731 @default.
- W3213231502 creator A5052677272 @default.
- W3213231502 creator A5065103755 @default.
- W3213231502 creator A5077862962 @default.
- W3213231502 creator A5082500328 @default.
- W3213231502 date "2021-11-09" @default.
- W3213231502 modified "2023-09-22" @default.
- W3213231502 title "MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps" @default.
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