Additional feature layers from ordered aggregations for deep 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 | 2021-03-10T13:53:54Z | |
| dc.date.available | 2021-08-26T23:00:15Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In the last years we have seen huge advancements in the area of Machine Learning, specially with the use of Deep Neural Networks. One of the most relevant examples is in image classification, where convolutional neural networks have shown to be a vital tool, hard to replace with any other techniques. Although aggregation functions, such as OWA operators, have been previously used on top of neural networks, usually to aggregate the outputs of different networks or systems (ensembles), in this paper we propose and explore a new way of using OWA aggregations in deep learning. We implement OWA aggregations as a new layer inside a convolutional neural network. These layers are used to learn additional order-based information from the feature maps of a certain layer, and then the newly generated information is used as a complement input for the following layers. We carry out several tests introducing the new layer in a VGG13-based reference network and show that this layer introduces new knowledge into the network without substantially increasing training times. | en |
| dc.description.sponsorship | This work was partially supported by the Public University of Navarre under the projects PJUPNA13 and PJUPNA1926 . | en |
| dc.embargo.lift | 2021-08-26 | |
| dc.embargo.terms | 2021-08-26 | |
| dc.format.extent | 8 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | I. Dominguez-Catena, D. Paternain and M. Galar, 'Additional Feature Layers from Ordered Aggregations for Deep Neural Networks,' 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/FUZZ48607.2020.9177555. | en |
| dc.identifier.doi | 10.1109/FUZZ48607.2020.9177555 | |
| dc.identifier.isbn | 978-1-7281-6932-3 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/39380 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 2020, pp. 1-8 | en |
| dc.relation.publisherversion | https://doi.org/10.1109/FUZZ48607.2020.9177555 | |
| dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Neural nets | en |
| dc.subject | RNN | en |
| dc.subject | Deep learning | en |
| dc.subject | OWA operator | en |
| dc.title | Additional feature layers from ordered aggregations for deep neural networks | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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