Transfer learning eases the burden of training a well-performed model from
scratch, especially when training data is scarce and computation power is
limited. In deep learning, a typical strategy for transfer learning is to
freeze the early layers of a pre-trained model and fine-tune the rest of its
layers on the target domain. Previous work focuses on the accuracy of the
transferred model but neglects the transfer of adversarial robustness. In this
work, we first show that transfer learning improves the accuracy on the target
domain but degrades the inherited robustness of the target model. To address
such a problem, we propose a novel cooperative adversarially-robust transfer
learning (CARTL) by pre-training the model via feature distance minimization
and fine-tuning the pre-trained model with non-expansive fine-tuning for target
domain tasks. Empirical results show that CARTL improves the inherited
robustness by about 28% at most compared with the baseline with the same degree
of accuracy. Furthermore, we study the relationship between the batch
normalization (BN) layers and the robustness in the context of transfer
learning, and we reveal that freezing BN layers can further boost the
robustness transfer.

本文研究了在深度学习中常用的迁移学习策略，探讨了始终关注模型精度而忽视模型鲁棒性问题的不足之处，并提出了一种基于特征距离最小化的协作式对抗鲁棒迁移学习方法（CARTL），以提高模型的鲁棒性。本文还就 BN 层对模型鲁棒性的影响进行研究，发现冻结 BN 层可以进一步提高模型的鲁棒性。