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Title: Preventing catastrophic forgetting in continual learning
Authors: Ong, Yi Shen
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Ong, Y. S. (2022). Preventing catastrophic forgetting in continual learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0626
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.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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