Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162924
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dc.contributor.authorOng, Yi Shenen_US
dc.date.accessioned2022-11-14T03:52:28Z-
dc.date.available2022-11-14T03:52:28Z-
dc.date.issued2022-
dc.identifier.citationOng, Y. S. (2022). Preventing catastrophic forgetting in continual learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162924en_US
dc.identifier.urihttps://hdl.handle.net/10356/162924-
dc.description.abstractContinual learning in neural networks has been receiving increased interest due to how prevalent machine learning is in an increasing number of industries. Catastrophic forgetting, which is when a model forgets old tasks upon learning new tasks, is still a major roadblock in allowing neural networks to be truly life-long learners. A series of tests were conducted on the effectiveness of using buffers filled with old training data as a way of mitigating forgetting by training them alongside new data. The results are that increasing the size of the buffer does help mitigate forgetting at the cost of increased space used.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0626en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titlePreventing catastrophic forgetting in continual learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Guoshengen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailgslin@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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