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 CLARE (Continual Learning Approach with pRE-trained models for RealCL scenarios), a pre-trained model-based solution designed to integrate new knowledge while preserving past learning. Our contributions include pioneering RealCL as a generalization of traditional CL setups, proposing CLARE as an adaptable approach for RealCL tasks, and conducting extensive experiments demonstrating its effectiveness across various RealCL scenarios. Notably, CLARE outperforms existing models on RealCL benchmarks, highlighting its versatility and robustness in unpredictable learning environments.

我们提出了一个新的连续学习（CL）范式Realistic Continual Learning（RealCL），其中任务之间的类分布是随机的，不同于结构化设置。同时，我们引入了一个名为CLARE的预训练模型解决方案，旨在在保留过去学习的同时整合新知识。通过广泛的实验证明了CLARE在各种RealCL场景中的有效性和优越性。

使用预训练模型的现实继续学习方法