Artificial Neural Networks and Deep Learning in Visual Arts: a Review

UDC.coleccionInvestigación
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.endPage157
UDC.grupoInvRNASA - IMEDIR (INIBIC)
UDC.grupoInvTecnoloxías Creativas e Intelixencia Artificial (TCIA)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.journalTitleNeural Computing and Applications
UDC.startPage121
UDC.volume33
dc.contributor.authorSantos, Iria
dc.contributor.authorCastro, M. Luz
dc.contributor.authorRodríguez-Fernández, Nereida
dc.contributor.authorTorrente-Patiño, Álvaro
dc.contributor.authorCarballal, Adrián
dc.date.accessioned2026-02-06T18:15:11Z
dc.date.available2026-02-06T18:15:11Z
dc.date.issued2021
dc.description“This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00521-020-05565-4. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms”
dc.description.abstract[Abstract]: In this article we make an intensive analysis of the use of Artificial Neural Network and Deep Learning in Visual Arts. We introduce the content on Artificial Intelligence over the years and examine in depth the latest work carried out in prediction, classi_cation, evaluation, generation, and identification through Artifiial Neural Networks for the different Visual Arts. We highlight the contributions of photography and pictorial artworks, but there are also other uses for 3D modeling, video games, architecture, or comics. The reported results of the different investigations mentioned show us that, in the field of Visual Arts, Artificial Neural Networks continue to evolve constantly and that lately they show significant growth. To complement the text, we include a table with information about the most employed image data sets and a glossary.
dc.description.sponsorshipThis work has also been supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G01, ED431D 201716), and Competitive Reference Groups (Ref. ED431C 201849).
dc.description.sponsorshipXunta de Galicia; ED431G01
dc.description.sponsorshipXunta de Galicia; ED431D 201716
dc.description.sponsorshipXunta de Galicia; ED431C 201849
dc.identifier.citationSantos, I., Castro, L., Rodriguez-Fernandez, N. et al. Artificial Neural Networks and Deep Learning in the Visual Arts: a review. Neural Comput & Applic 33, 121–157 (2021). https://doi.org/10.1007/s00521-020-05565-4
dc.identifier.doi10.1007/s00521-020-05565-4
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttps://hdl.handle.net/2183/47287
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.urihttps://doi.org/10.1007/s00521-020-05565-4
dc.rights.accessRightsopen access
dc.subjectArtificial Neural Networks
dc.subjectGenerative Adversarial Networks
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectVisual Arts
dc.subjectMachine Learning
dc.subjectPrediction
dc.subjectClassification
dc.subjectEvaluation
dc.subjectGeneration
dc.subjectIdentification
dc.subjectTransfer Learning
dc.subjectDatasets
dc.titleArtificial Neural Networks and Deep Learning in Visual Arts: a Review
dc.typejournal article
dc.type.hasVersionAM
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery4b05c41f-26dc-44a6-8928-adf2847aae27

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