Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, and then fine-tuning the last few layers in order to capture specific information related to the target situation. This approach is particularly useful when only limited or weakly labelled data are available for the new task. In this work, we demonstrate that adversarially-trained models transfer better across new domains than naturally-trained models, even though it's known that these models do not generalize as well as naturally-trained models on the source domain. We show that this behavior results from a bias, introduced by the adversarial training, that pushes the learned inner layers to more natural image representations, which in turn enables better transfer.

本文通过研究发现，针对新领域的迁移学习中，对于只有有限数据的情况下，通过对抗训练的模型相对于非对抗训练的模型具有更好的迁移性能。同时，对抗训练可以使学习到的表示偏向于保留形状而不是纹理，这影响了源模型的可迁移性，并且使用影响函数的方法发现迁移的对抗训练模型包含更多的人类可识别的语义信息，这至少部分解释了为什么对抗训练模型更实用。

对抗训练的深度神经网络转移更佳: 以图像分类为例