Understanding trained CNNs by indexing neuron selectivity

1 Feb 2017Ivet RafegasMaria VanrellLuis A. AlexandreGuillem Arias

The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties... (read more)

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