MaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Wolverhampton-
Autor(es): dc.creatorSantos, Claudio Filipi Goncalves dos-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T21:35:50Z-
Data de disponibilização: dc.date.available2025-08-21T21:35:50Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-04881-4_22-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241826-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241826-
Descrição: dc.descriptionIn the last decade, exponential data growth supplied the machine learning-based algorithms’ capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process since training complex models denotes an expensive task and results are prone to overfit the training data. A supervised regularization technique called MaxDropout was recently proposed to tackle the latter, providing several improvements concerning traditional regularization approaches. In this paper, we present its improved version called MaxDropoutV2. Results considering two public datasets show that the model performs faster than the standard version and, in most cases, provides more accurate results.-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionFederal University of São Carlos-
Descrição: dc.descriptionSão Paulo State University-
Descrição: dc.descriptionUniversity of Wolverhampton-
Descrição: dc.descriptionSão Paulo State University-
Formato: dc.format271-282-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Título: dc.titleMaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.