  Knowledge transferability, or transfer learning, has been widely adopted to
allow a pre-trained model in the source domain to be effectively adapted to
downstream tasks in the target domain. It is thus important to explore and
understand the factors affecting knowledge transferability. In this paper, as
the first work, we analyze and demonstrate the connections between knowledge
transferability and another important phenomenon--adversarial transferability,
\emph{i.e.}, adversarial examples generated against one model can be
transferred to attack other models. Our theoretical studies show that
adversarial transferability indicates knowledge transferability and vice versa.
Moreover, based on the theoretical insights, we propose two practical
adversarial transferability metrics to characterize this process, serving as
bidirectional indicators between adversarial and knowledge transferability. We
conduct extensive experiments for different scenarios on diverse datasets,
showing a positive correlation between adversarial transferability and
knowledge transferability. Our findings will shed light on future research
about effective knowledge transfer learning and adversarial transferability
analyses.
