Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition

arXiv:2405.03244v1 Announce Type: new
Abstract: Continual learning (CL) has spurred the development of several methods aimed at consolidating previous knowledge across sequential learning. Yet, the evaluations of these methods have primarily focused on the final output, such as changes in the accuracy of predicted classes, overlooking the issue of representational forgetting within the model. In this paper, we propose a novel representation-based evaluation framework for CL models. This approach involves gathering internal representations from throughout the continual learning process and …

abstract accuracy arxiv continual cs.lg development issue knowledge tensor through type