Probenet: Probing Deep Networks

Abstract

Despite the rapid progress of deep learning research in recent years, interpreting deep network is still quite challenging. Interpreting deep networks is essential to both end-users and developers since it gives confidence in the usage of the deep network. This paper deals with a method for interpreting deep networks, especially visual interpretation. In order to get visual interpretation from a target deep network, we propose a ProbeNet that provides a decomposed visual interpretation of the target deep network. The ProbeNet decomposes the feature representations of the point of the target deep network into human interpretable units. Furthermore, the ProbeNet provides kernel-level analysis about the target deep network. In experiments, visual interpretation of two different target deep networks showed the usefulness of the ProbeNet to interpret target deep networks.