Maintaining Plasticity in Deep Continual Learning

arXiv:2306.13812v3 Announce Type: replace
Abstract: Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occurs continually. If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail to remember earlier examples. More fundamental, but less well known, is that they may also lose their ability to learn on new examples, a phenomenon called loss of plasticity. …

abstract arxiv continual continual-learning cs.lg learning systems modern systems training type