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While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains.
A Lifelong Learning Perspective for Mobile Robot Control
Sebastian Thrun · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Earlier work this paper cites.
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Earlier work this paper cites.
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