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A Study on Efficiency in Continual Learning Inspired by Human Learning
[article]
2020
arXiv
pre-print
Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capacity and time while not suffering from catastrophic forgetting. In this work we study the efficiency of continual learning systems, taking inspiration from human learning. In particular, inspired by the mechanisms of sleep, we evaluate popular pruning-based continual learning algorithms, using PackNet as a case study.
arXiv:2010.15187v1
fatcat:by6lw5bnpjd7zmn4ouela6r6nm