A study of OWA operators learned in convolutional neural networks
| dc.contributor.author | Domínguez Catena, Iris | |
| dc.contributor.author | Paternain Dallo, Daniel | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
| dc.date.accessioned | 2022-01-12T08:17:13Z | |
| dc.date.available | 2022-01-12T08:17:13Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the expo-nential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations. | en |
| dc.description.sponsorship | This work was funded by a predoctoral fellowship of the Research Service of Universidad Pública de Navarra, the Universidad Pública de Navarra under project PJUPNA1926, and the Spanish MICIN (PID2019-108392GB-I00 / AEI / 10.13039/501100011033). | en |
| dc.format.extent | 20 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/app11167195 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41733 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Applied Sciences, 11 (16), 7195 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/app11167195 | |
| dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | OWA operator | en |
| dc.subject | Aggregation functions | en |
| dc.subject | Orness | en |
| dc.subject | Convolutional neural network | en |
| dc.subject | Deep learning | en |
| dc.title | A study of OWA operators learned in convolutional neural networks | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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