Nowadays, real-world applications often face streaming data, which requires
the learning system to absorb new knowledge as data evolves. Continual Learning
(CL) aims to achieve this goal and meanwhile overcome the catastrophic
forgetting of former knowledge when learning new ones. Typical CL methods build
the model from scratch to grow with incoming data. However, the advent of the
pre-trained model (PTM) era has sparked immense research interest, particularly
in leveraging PTMs' robust representational capabilities. This paper presents a
comprehensive survey of the latest advancements in PTM-based CL. We categorize
existing methodologies into three distinct groups, providing a comparative
analysis of their similarities, differences, and respective advantages and
disadvantages. Additionally, we offer an empirical study contrasting various
state-of-the-art methods to highlight concerns regarding fairness in
comparisons. The source code to reproduce these evaluations is available at:
this https URL

本文是对基于预训练模型的持续学习最新进展的综合调查，将现有方法分为三组进行分类，提供了它们的相似性、差异性、以及各自的优缺点的比较分析，并通过实证研究对比了各种最先进的方法，以凸显比较中公平性方面的关注点。