Modification of information reduction processes in Convolutional Neural Networks

Simple item page
dc.contributor.advisorBustince Sola, Humberto
dc.contributor.advisorHerrera, Francisco
dc.contributor.advisorTakáč, Zdenko
dc.contributor.authorRodríguez Martínez, Iosu
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.date.accessioned2024-10-16T11:44:26Z
dc.date.available2024-10-16T11:44:26Z
dc.date.issued2024
dc.date.submitted2024-10-11
dc.description.abstractDuring the last decade, Deep Artificial Neural Networks have established themselves as the state-of-the-art solution for solving complex tasks such as image processing, time-series forecasting, or natural language processing. One of the most studied families of artificial neural network is that of Convolutional Neural Networks (CNNs), which can exploit the local information of data sources such as images by automatically extracting increasingly more complex features in a hierarchical manner. Although plenty of work has been dedicated to the introduction of more complex (or more efficient) model architectures of CNN; to solving the optimisation problems faced by them and accelerating training convergence; or to trying to interpret their inner workings as well as explaining their generated predictions, an important key aspect of these models is sometimes overlooked: that of feature fusion. Feature fusion appears in plenty of forms in CNNs. Feature downsampling is necessary in order to compress the intermediate representations generated by the model, while preserving the most relevant information, a process which also makes models robust to small shifts in the inputs. Combining different sources of data or different feature representations is also a recurrent problem in neural networks, which is usually taken care of by simply allowing the model to learn additional transformations in a supervised manner, increasing its parameter count. In this dissertation, we study the application of solutions of the Information Fusion field to better tackle these problems. In particular, we explore the use of aggregation functions which replace a set of input values by a suitable single representative. We study the most important properties of these functions in the context of CNN feature reduction, and present novel pooling and Global Pooling proposals inspired by our discoveries. We also test the suitability of our proposals for the detection of COVID-19 patients, presenting an end-to-end pipeline which automatically analyses chest x-ray images.en
dc.description.doctorateProgramPrograma de Doctorado en Ciencias y Tecnologías Industriales (RD 99/2011)es_ES
dc.description.doctorateProgramIndustria Zientzietako eta Teknologietako Doktoretza Programa (ED 99/2011)eu
dc.format.extent99 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.doi10.48035/Tesis/2454/52294
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/52294
dc.language.isoeng
dc.relation.publisherversionhttps://doi.org/10.48035/Tesis/2454/52294
dc.rightsCreative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/deed.es/
dc.subjectConvolutional Neural Networksen
dc.subjectFeature fusionen
dc.subjectDetection of COVID-19en
dc.subjectAggregation functionsen
dc.titleModification of information reduction processes in Convolutional Neural Networksen
dc.title.alternativeModificación de procesos de reducción de información en Redes Neuronales Convolucionaleses_ES
dc.typeinfo:eu-repo/semantics/doctoralThesis
dspace.entity.typePublication
relation.isAdvisorOfPublication1bdd7a0e-704f-48e5-8d27-4486444f82c9
relation.isAdvisorOfPublication.latestForDiscovery1bdd7a0e-704f-48e5-8d27-4486444f82c9
relation.isAuthorOfPublication70c54ba9-626d-461e-aa8a-a3f99f35ba13
relation.isAuthorOfPublication.latestForDiscovery70c54ba9-626d-461e-aa8a-a3f99f35ba13

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
01 Tesis_RodriguezMartinez.pdf
Size:
41.34 MB
Format:
Adobe Portable Document Format
Download

Collections

Tesis doctorales DEIM - EIMS Doktoretza tesiak
Tesis doctorales - Doktoretza tesiak