Realistic Continual Learning Approach using Pre-trained Models

arXiv:2404.07729v1 Announce Type: new
Abstract: Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forgetting, where models lose proficiency in previously learned tasks as they acquire new ones. While numerous solutions have been proposed, existing experimental setups often rely on idealized class-incremental learning scenarios. We introduce Realistic Continual Learning (RealCL), a novel CL paradigm where class distributions across tasks are random, departing from structured setups.
We also present …

abstract adaptability arxiv catastrophic forgetting challenge continual cs.cv cs.lg experimental knowledge ones pre-trained models research solutions tasks type