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Subject [ICIP 2019] Probenet: Probing Deep Networks (by Jae-Hyeok Lee) is accepted in IEEE ICIP2019
Name
Date 2019-05-01
Title: Probenet: Probing Deep Networks

Authors: Jae-Hyeok Lee, Seong Tae Kim, and Yong Man Ro

Abstract: Despite the rapid progress of deep learning research in re-cent 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 ProbeNet that provides a decomposed visual interpretation of the target deep network. The ProbeNet de-composes the feature representations of the point of the tar-get deep network into human interpretable units. Further-more, 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.