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[2407.17120] Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Summary
This article explores Parameter-Efficient Fine-Tuning for Continual Learning (PEFT-CL) using Neural Tangent Kernel (NTK) theory, addressing performance metrics and proposing a novel framework to improve task adaptability and mitigate forgetting.
Why It Matters
Understanding PEFT-CL is crucial for advancing continual learning systems, which are increasingly relevant in AI applications. This research provides theoretical insights that can enhance model performance and efficiency, addressing a significant challenge in machine learning.
Key Takeaways
- PEFT-CL helps adapt pre-trained models to sequential tasks while reducing forgetting.
- NTK theory provides a framework for analyzing performance metrics in continual learning.
- The proposed NTK-CL framework improves feature representation and reduces generalization gaps.
- Key factors influencing performance include training sample size, feature orthogonality, and regularization.
- This research contributes to the development of more efficient continual learning systems.
Computer Science > Machine Learning arXiv:2407.17120 (cs) [Submitted on 24 Jul 2024 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective Authors:Jingren Liu, Zhong Ji, YunLong Yu, Jiale Cao, Yanwei Pang, Jungong Han, Xuelong Li View a PDF of the paper titled Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective, by Jingren Liu and 6 other authors View PDF HTML (experimental) Abstract:Parameter-efficient fine-tuning for continual learning (PEFT-CL) has shown promise in adapting pre-trained models to sequential tasks while mitigating catastrophic forgetting problem. However, understanding the mechanisms that dictate continual performance in this paradigm remains elusive. To unravel this mystery, we undertake a rigorous analysis of PEFT-CL dynamics to derive relevant metrics for continual scenarios using Neural Tangent Kernel (NTK) theory. With the aid of NTK as a mathematical analysis tool, we recast the challenge of test-time forgetting into the quantifiable generalization gaps during training, identifying three key factors that influence these gaps and the performance of PEFT-CL: training sample size, task-level feature orthogonality, and regularization. To address these challenges, we introduce NTK-CL, a novel framework that eliminates task-specific parameter storage while adaptively generating task-relevant features. Aligning with theoreti...
