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dc.contributor.authorCastro, Francisco M.
dc.contributor.authorMarín-Jiménez, Manuel J.
dc.contributor.authorGuil-Mata, Nicolas 
dc.contributor.authorSchmid, Cordelia
dc.contributor.authorAlahari, Karteek
dc.date.accessioned2019-11-25T13:11:54Z
dc.date.available2019-11-25T13:11:54Z
dc.date.created2019
dc.date.issued2019-11-25
dc.identifier.urihttps://hdl.handle.net/10630/18907
dc.description.abstractwhen new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present a way to include new information in a previously trained model without training from scratch and using a small subset of old data. We perform a thorough experimental evaluation of the proposed approach on two image classification datasets: CIFAR-100 and ImageNet. The experiment results show that it is possible to include new knowledge in a model without forgetting the previous one, although, the performance is still lower than training from scratch with the complete training set.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectReconocimiento óptico de formas (Informática)en_US
dc.subjectRedes neuronales (Informática)en_US
dc.subject.otherGait recognitionen_US
dc.subject.otherIncremental learningen_US
dc.subject.otherConvolutional Neural Networken_US
dc.titleGait recognition applying Incremental learningen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle2019 International Workshop on Human Identification at a Distanceen_US
dc.relation.eventplaceSouthern University of Science and Technology, Shenzhen, Chinaen_US
dc.relation.eventdate22/11/2019en_US
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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