Adapt before Continual Learning
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.
6841396eee27975702b57e87
Aojun Lu
claimed_verified
2025-06-05T07:26:29.156Z
6759546743971eff5a12a087
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/esJm_83zW1R6NqWltof8P.png
Aojun Lu
user
Kurt1024
6841396eee27975702b57e88
Tao Feng
6841396eee27975702b57e89
Hangjie Yuan
claimed_verified
2025-06-05T07:26:31.411Z
649d54b314afbb10ce2a9eeb
/avatars/15c325d8c2273ff63569f23015e98486.svg
Hangjie Yuan
user
JacobYuan
6841396eee27975702b57e8a
Chunhui Ding
6841396eee27975702b57e8b
Yanan Sun
Adapt before Continual Learning
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.
Adapting Pre-trained Models before the core CL process (ACL) improves Continual Learning by enhancing plasticity while maintaining stability.
Continual Learning CL Pre-trained models PTMs plasticity stability domain gaps catastrophic forgetting prompt tuning embeddings class prototypes