AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low Time and Resource Consumption

arXiv:2403.15751v1 Announce Type: new
Abstract: Online Class Incremental Learning (OCIL) aims to train the model in a task-by-task manner, where data arrive in mini-batches at a time while previous data are not accessible. A significant challenge is known as Catastrophic Forgetting, i.e., loss of the previous knowledge on old data. To address this, replay-based methods show competitive results but invade data privacy, while exemplar-free methods protect data privacy but struggle for accuracy. In this paper, we proposed an exemplar-free approach …

abstract arxiv catastrophic forgetting challenge class consumption cs.cv data free incremental knowledge loss low train type