  Transfer learning has emerged as a powerful methodology for adapting
pre-trained deep neural networks on image recognition tasks 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 labeled data are available for
the new task. In this work, we demonstrate that adversarially-trained models
transfer better than non-adversarially-trained models, especially if only
limited data are available for the new domain task. Further, we observe that
adversarial training biases the learnt representations to retaining shapes, as
opposed to textures, which impacts the transferability of the source models.
Finally, through the lens of influence functions, we discover that transferred
adversarially-trained models contain more human-identifiable semantic
information, which explains -- at least partly -- why adversarially-trained
models transfer better.
