Adversarially robust transfer learning

Understand

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.