Safety-Oriented Stability Biases for Continual Learning

dc.contributor.authorGaurav, Ashish
dc.date.accessioned2020-01-24T19:21:24Z
dc.date.available2020-01-24T19:21:24Z
dc.date.issued2020-01-24
dc.date.submitted2020-01-21
dc.description.abstractContinual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that are safety-validated prior to deployment, it is essential to ensure that validated knowledge is retained. We propose methods that build on existing unconstrained continual learning solutions, which increase the model variance or weaken the model bias to better retain more of the existing knowledge. We investigate multiple such strategies, both for continual classification as well as continual reinforcement learning. Finally, we demonstrate the improved performance of our methods against popular continual learning approaches, using variants of standard image classification datasets, as well as assess the effect of weaker biases in continual reinforcement learning.en
dc.identifier.urihttp://hdl.handle.net/10012/15579
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectdeep learningen
dc.subjectcontinual learningen
dc.subjectclassificationen
dc.subjectreinforcement learningen
dc.titleSafety-Oriented Stability Biases for Continual Learningen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws.contributor.advisorCzarnecki, Krzysztof
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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