  Exploring the intrinsic interconnections between the knowledge encoded in
PRe-trained Deep Neural Networks (PR-DNNs) of heterogeneous tasks sheds light
on their mutual transferability, and consequently enables knowledge transfer
from one task to another so as to reduce the training effort of the latter. In
this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the
transferability of knowledge learned from PR-DNNs. In DEPARA, nodes correspond
to the inputs and are represented by their vectorized attribution maps with
regards to the outputs of the PR-DNN. Edges denote the relatedness between
inputs and are measured by the similarity of their features extracted from the
PR-DNN. The knowledge transferability of two PR-DNNs is measured by the
similarity of their corresponding DEPARAs. We apply DEPARA to two important yet
under-studied problems in transfer learning: pre-trained model selection and
layer selection. Extensive experiments are conducted to demonstrate the
effectiveness and superiority of the proposed method in solving both these
problems. Code, data and models reproducing the results in this paper are
available at \url{https://github.com/zju-vipa/DEPARA}.

Computer Vision and Pattern Recognition