  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:
https://github.com/sun-hailong/LAMDA-PILOT
