Semi-automatic CNN Architectural Pruning using the Bayesian Case Model and Dimensionality Reduction Visualization

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dc.contributor.authorMarcilio-Jr, Wilson E. [UNESP]
dc.contributor.authorEler, Danilo [UNESP]
dc.contributor.authorGuilherme, Ivan [UNESP]
dc.contributor.authorHurter, C.
dc.contributor.authorPurchase, H.
dc.contributor.authorBouatouch, K.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-11-30T13:42:21Z
dc.date.available2022-11-30T13:42:21Z
dc.date.issued2022-01-01
dc.description.abstractVisualization techniques have been applied to reasoning about complex machine learning models. These visual approaches aim to enhance the understanding of black-box models' decisions or guide in hyperparameters configuration, such as the number of layers and neurons/filters in deep neural networks. While several works address the architectural tuning of convolutional neural networks (CNNs), only a few works face the problem from a semi-automatic perspective. This work presents a novel application of the Bayesian Case Model that uses visualization strategies to convey the most important filters of convolutional layers for image classification. A heatmap coordinated with a scatterplot visualization emphasizes the filters with the most contribution to the CNN prediction. Our methodology is evaluated on a case study using the MNIST dataset.en
dc.description.affiliationSao Paulo State Univ UNESP, Dept Math & Comp Sci, Presidente Prudente, SP, Brazil
dc.description.affiliationSao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, Rio Claro, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Dept Math & Comp Sci, Presidente Prudente, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, Rio Claro, SP, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2018/17881-3
dc.description.sponsorshipIdFAPESP: 2018/25755-8
dc.format.extent203-209
dc.identifierhttp://dx.doi.org/10.5220/0010991000003124
dc.identifier.citationProceedings Of The 17th International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications (ivapp), Vol 3. Setubal: Scitepress, p. 203-209, 2022.
dc.identifier.doi10.5220/0010991000003124
dc.identifier.issn2184-4321
dc.identifier.urihttp://hdl.handle.net/11449/237700
dc.identifier.wosWOS:000777508400019
dc.language.isoeng
dc.publisherScitepress
dc.relation.ispartofProceedings Of The 17th International Joint Conference On Computer Vision, Imaging And Computer Graphics Theory And Applications (ivapp), Vol 3
dc.sourceWeb of Science
dc.subjectCNN Pruning
dc.subjectCase-based Reasoning
dc.subjectVisualization
dc.titleSemi-automatic CNN Architectural Pruning using the Bayesian Case Model and Dimensionality Reduction Visualizationen
dc.typeTrabalho apresentado em evento
dcterms.rightsHolderScitepress
unesp.departmentMatemática e Computação - FCTpt

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Presidente Prudente - FCT - Faculdade de Ciências e Tecnologia
Artigos - Matemática - IGCE