DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images

arXiv:2404.14099v1 Announce Type: new
Abstract: Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approaches, including memory replay, knowledge distillation, model regularization, and dynamic network expansion, have been proposed to address this issue. Thus far, dynamic network expansion methods have achieved state-of-the-art performance at the cost of incurring significant computational overhead. This is due to the need for additional model buffers, which makes it less feasible …

arxiv class cs.cv dynamic images incremental medical merging type