Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning

CVPR 2019 Oleksiy OstapenkoMihai PuscasTassilo KleinPatrick JähnichenMoin Nabi

Models trained in the context of continual learning (CL) should be able to learn from a stream of data over an undefined period of time. The main challenges herein are: 1) maintaining old knowledge while simultaneously benefiting from it when learning new tasks, and 2) guaranteeing model scalability with a growing amount of data to learn from... (read more)

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