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https://hdl.handle.net/10356/162924
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ong, Yi Shen | en_US |
dc.date.accessioned | 2022-11-14T03:52:28Z | - |
dc.date.available | 2022-11-14T03:52:28Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Ong, Y. S. (2022). Preventing catastrophic forgetting in continual learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162924 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/162924 | - |
dc.description.abstract | Continual 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.iso | en | en_US |
dc.publisher | Nanyang Technological University | en_US |
dc.relation | SCSE21-0626 | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.title | Preventing catastrophic forgetting in continual learning | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | Lin Guosheng | en_US |
dc.contributor.school | School of Computer Science and Engineering | en_US |
dc.description.degree | Bachelor of Engineering (Computer Science) | en_US |
dc.contributor.supervisoremail | gslin@ntu.edu.sg | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
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FYP Report.pdf Restricted Access | 4.37 MB | Adobe PDF | View/Open |
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