Adapt before Continual Learning

2025-06-04

AI Summary

Continual learning (CL) of pretrained models faces a fundamental trade-off between stability and plasticity, especially under significant distribution shifts across domains. To address this, we propose a “adapt-then-learn” paradigm: before processing each new task, a lightweight, prototype-guided adaptation is applied to the backbone network, followed by standard CL training. This adaptation aligns embedding spaces via prototype-aware optimization, theoretically unifying class-prototype consistency and feature discriminability—thereby overcoming the performance bottleneck inherent in frozen or full-parameter fine-tuning. The adaptation module is plug-and-play, seamlessly integrating with mainstream CL frameworks such as Prompt Tuning. Extensive experiments on multiple benchmarks demonstrate that our method significantly mitigates catastrophic forgetting, enhances cross-domain transfer capability, and improves generalization across tasks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Balancing stability and plasticity in continual learning
• Addressing domain gaps in incremental tasks with PTMs
• Preventing catastrophic forgetting while adapting pre-trained models

Innovation

• Adapts pre-trained models before continual learning
• Aligns embeddings with original class prototypes
• Balances stability and plasticity in learning