Continual Unsupervised Representation Learning

NeurIPS 2019 Dushyant RaoFrancesco VisinAndrei RusuRazvan PascanuYee Whye TehRaia Hadsell

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries... (read more)

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