从神经正切核视角揭示模型重编程的成功机制。
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
- 用神经正切核分析重编程的理论基础，揭示其依赖目标数据的特征谱。
- 发现源模型性能决定重编程效果，关键在源-目标数据的匹配度。
- 为高效微调提供新理论支持，适合关注模型泛化与轻量适配的研究者。
模型重编程（MR）是一种资源高效的框架，仅用少量新增参数和数据即可将大预训练模型适配到新任务，是应对多样化任务训练大模型挑战的有力方案。尽管其在计算机视觉、时间序列预测等多个领域表现出色，但其理论基础仍不清晰。本文通过神经正切核（NTK）框架对MR进行系统性理论分析，证明其成功由目标数据上NTK矩阵的特征值谱决定，并揭示源模型的有效性在重编程结果中起关键作用。本文提出新的理论框架，深化了对源-目标模型关系的理解，并通过大量实验验证了理论发现。
原文摘要 · Abstract (English)
Model Reprogramming (MR) is a resource-efficient framework that adapts large pre-trained models to new tasks with minimal additional parameters and data, offering a promising solution to the challenges of training large models for diverse tasks. Despite its empirical success across various domains such as computer vision and time-series forecasting, the theoretical foundations of MR remain underexplored. In this paper, we present a comprehensive theoretical analysis of MR through the lens of the Neural Tangent Kernel (NTK) framework. We demonstrate that the success of MR is governed by the eigenvalue spectrum of the NTK matrix on the target dataset and establish the critical role of the source model's effectiveness in determining reprogramming outcomes. Our contributions include a novel theoretical framework for MR, insights into the relationship between source and target models, and extensive experiments validating our findings.
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