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Understanding trained CNNs by indexing neuron selectivity

dc.contributor.authorRafegas, Ivet
dc.contributor.authorVanrell, Maria
dc.contributor.authorAlexandre, Luís
dc.contributor.authorArias, Guillem
dc.date.accessioned2020-01-09T11:08:20Z
dc.date.available2020-01-09T11:08:20Z
dc.date.issued2017-02-01
dc.description.abstractThe 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. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.patrec.2019.10.013pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8151
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.titleUnderstanding trained CNNs by indexing neuron selectivitypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titlePattern Recognition Letterspt_PT
person.familyNameAlexandre
person.givenNameLuís
person.identifier.ciencia-id2014-0F06-A3E3
person.identifier.orcid0000-0002-5133-5025
person.identifier.ridE-8770-2013
person.identifier.scopus-author-id8847713100
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication131ec6eb-b61a-4f27-953f-12e948a43a96
relation.isAuthorOfPublication.latestForDiscovery131ec6eb-b61a-4f27-953f-12e948a43a96

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