Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computational limitations become even more cumbersome. We consider robust transfer learning, in which we transfer not only performance but also robustness from a source model to a target domain. We start by observing that robust networks contain robust feature extractors. By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks. We then consider the case of fine-tuning a network by re-training end-to-end in the target domain. When using lifelong learning strategies, this process preserves the robustness of the source network while achieving high accuracy. By using such strategies, it is possible to produce accurate and robust models with little data, and without the cost of adversarial training.

本文研究如何在数据稀缺或者训练成本较高的情况下，通过对源模型的继承和微调，使得目标模型不仅精度高，而且对抗攻击具有良好的鲁棒性，其中运用到Transfer Learning， Neural Network Classifiers，Robustness，Lifelong Learning和Generalization等关键词。

对抗性稳健的迁移学习