Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155416
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dc.contributor.authorDang, Zhangen_US
dc.date.accessioned2022-02-23T02:19:50Z-
dc.date.available2022-02-23T02:19:50Z-
dc.date.issued2021-
dc.identifier.citationDang, Z. (2021). Modified-LwF method for continual learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155416en_US
dc.identifier.urihttps://hdl.handle.net/10356/155416-
dc.description.abstractIn this dissertation, we show that it is possible to overcome the catastrophic forgetting with several different methods. What is more important is that our method remembers old tasks better by combining the original learning without forgetting and elastic weight consolidation, which is the main contribution that both the merits of elastic weight consolidation and learning without forgetting are put into one method (Modified LwF). Besides, the upper bound joint training method, fine tune, EWC and original LwF methods are experimented by adding the new tasks one by one. In this procedure, the paths of the training in the algorithm will be focused more on. We finally finished all four tasks, and the size of the fourth task is far bigger than the previous three.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleModified-LwF method for continual learningen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorPonnuthurai Nagaratnam Suganthanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailEPNSugan@ntu.edu.sgen_US
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