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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1275
DC FieldValueLanguage
dc.contributor.authorLu Minh Khuongen_US
dc.date.accessioned2022-01-18T04:26:01Z-
dc.date.available2022-01-18T04:26:01Z-
dc.date.issued2021-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/1275-
dc.description.abstractThis thesis introduces a new approach of open set recognition that prevents catastrophic forgetting in deep continual learning. A single model combines a joint probabilistic encoder with a generative model and a linear classifier, all of which are shared among tasks that arrive in sequence. It is developed based on the Variational Auto Encoder (VAE) then wrap the encoder and decoder in a Wide Residual Network (Wide Res Net) to increase the model's accuracyen_US
dc.language.isoenen_US
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDeep continual learningen_US
dc.titleOpen set recognition for deep continual learningen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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