Adapt before Continual Learning
Authors: name: Aojun Lu, name: Tao Feng, name: Hangjie Yuan, name: Chunhui Ding, name: Yanan Sun

Continual Learning (CL) seeks to enable neural networks to incrementally
acquire new knowledge (plasticity) while retaining existing knowledge
(stability). While pre-trained models (PTMs) have become pivotal in CL,
prevailing approaches freeze the PTM backbone to preserve stability, limiting
their plasticity, particularly when encountering significant domain gaps in
incremental tasks. Conversely, sequentially finetuning the entire PTM risks
catastrophic forgetting of generalizable knowledge, exposing a critical
stability-plasticity trade-off. To address this challenge, we propose Adapting
PTMs before the core CL process (ACL), a novel framework that refines the PTM
backbone through a plug-and-play adaptation phase before learning each new task
with existing CL approaches (e.g., prompt tuning). ACL enhances plasticity by
aligning embeddings with their original class prototypes while distancing them
from others, theoretically and empirically shown to balance stability and
plasticity. Extensive experiments demonstrate that ACL significantly improves
CL performance across benchmarks and integrated methods, offering a versatile
solution for PTM-based CL.