Exemplar-Free Class Incremental Learning via Incremental Representation

arXiv:2403.16221v1 Announce Type: new
Abstract: Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any old-class exemplars (i.e., samples). For this purpose, various efCIL methods have been proposed over the past few years, generally with elaborately constructed old pseudo-features, increasing the difficulty of model development and interpretation. In contrast, we propose a \textbf{simple Incremental Representation (IR) framework} for efCIL without constructing old pseudo-features. IR utilizes dataset augmentation to …

abstract arxiv class cs.cv features free incremental information knowledge representation samples type via