Meta-Aggregating Networks for Class-Incremental Learning

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. The inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes but high-stability models are weak to learn new classes... (read more)

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