Understanding Deep Architectures by Visual Summaries

27 Jan 2018Marco CarlettiMarco GodiMaedeh AghaeiFrancesco GiuliariMarco Cristani

In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class... (read more)

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