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dc.contributorMorros Rubió, Josep Ramon
dc.contributorSayrol Clols, Elisa
dc.contributor.authorVilardi, Alessandro Luca
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-09-21T12:44:42Z
dc.date.available2016-09-21T12:44:42Z
dc.date.issued2016-07
dc.identifier.urihttp://hdl.handle.net/2117/90109
dc.descriptionIn very recent years, several classification problems in computer vision, have boosted its performance by using Deep Learning techniques, in particular Convolutional Neural Networks (CNNs). The topic of the research project will focus in exploring state of the art deep learning architectures in computer vision applications. Recently architectures like GoogleNet and VGG have shown to perform better than other architectures.
dc.description.abstractThe goal of this thesis is to evaluate the face identification problem using very deep convolutional neural networks. In recent years, the use of CNN, with a large amount of images in databases, have made the deep learning technique very performant. The problems in training a network from scratch, such as having sufficient hardware resources and large databases, can be overcome using the finetune technique on pretrained models. This thesis evaluate the performance in finetuning for face classification the most recent CNN architectures which have obtained the best results at ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the last years, in particular VGG, GoogLeNet and ResNet. All the pre-trained models of the CNNs were downloaded from the MatConvNet website. VGG-16 has shown best results in face classification which was followed with ResNet-101 and GoogLeNet that are the matter of this thesis.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsS'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherCNN
dc.subject.otherface classification
dc.subject.otherfintuning
dc.subject.otherVGG
dc.subject.otherResNet
dc.subject.otherGoogLeNet
dc.titleVery deep convolutional neural networks for face identification
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.identifier.slugETSETB-230.120195
dc.rights.accessOpen Access
dc.date.updated2016-09-14T05:51:13Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona


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