A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks

Registro completo de metadados
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorRoder, Mateus [UNESP]-
Autor(es): dc.creatorPassos, Leandro A. [UNESP]-
Autor(es): dc.creatorRibeiro, Luiz Carlos Felix [UNESP]-
Autor(es): dc.creatorPereira, Clayton [UNESP]-
Autor(es): dc.creatorPapa, João Paulo [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:52:37Z-
Data de disponibilização: dc.date.available2022-02-22T00:52:37Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-61401-0_22-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208175-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208175-
Descrição: dc.descriptionWith the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, “very deep” models were emerging, once they were expected to extract more intrinsic and abstract features while supporting a better performance. However, such models suffer from the gradient vanishing problem, i.e., backpropagation values become too close to zero in their shallower layers, ultimately causing learning to stagnate. Such an issue was overcome in the context of convolution neural networks by creating “shortcut connections” between layers, in a so-called deep residual learning framework. Nonetheless, a very popular deep learning technique called Deep Belief Network still suffers from gradient vanishing when dealing with discriminative tasks. Therefore, this paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining, that support better discriminative performance. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification.-
Descrição: dc.descriptionSão Paulo State University - UNESP-
Descrição: dc.descriptionSão Paulo State University - UNESP-
Formato: dc.format231-241-
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-
Palavras-chave: dc.subjectDeep Belief Networks-
Palavras-chave: dc.subjectResidual networks-
Palavras-chave: dc.subjectRestricted Boltzmann Machines-
Título: dc.titleA Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.