Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach

arXiv:2403.18258v1 Announce Type: new
Abstract: This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data. GCIL is one of the hot topics in the field of computer vision, and this is considered one of the crucial tasks in society, specifically the continual learning of generative models. The ability to forget is a crucial brain function that facilitates continual learning by selectively …

abstract arxiv class computer computer vision cs.ai cs.cv data generative hot incremental information novel performance streaming streaming data study topics type vision