Optimizing Specific and Shared Parameters for Efficient Parameter Tuning

• URL: http://arxiv.org/abs/2504.03450v1
• Date: Fri, 04 Apr 2025 13:43:54 GMT
• Title: Optimizing Specific and Shared Parameters for Efficient Parameter Tuning
• Authors: Van-Anh Nguyen, Thanh-Toan Do, Mehrtash Harandi, Dinh Phung, Trung Le,
• Abstract summary: We propose SaS, a novel PETL method that effectively mitigates distributional shifts during fine-tuning.<n>SaS captures common statistical characteristics across layers using low-rank projections.<n>Experiments on diverse downstream tasks, few-shot settings and domain generalization demonstrate that SaS significantly enhances performance.
• Score: 46.57365875007367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models, with a vast number of parameters and pretraining on massive datasets, achieve state-of-the-art performance across various applications. However, efficiently adapting them to downstream tasks with minimal computational overhead remains a challenge. Parameter-Efficient Transfer Learning (PETL) addresses this by fine-tuning only a small subset of parameters while preserving pre-trained knowledge. In this paper, we propose SaS, a novel PETL method that effectively mitigates distributional shifts during fine-tuning. SaS integrates (1) a shared module that captures common statistical characteristics across layers using low-rank projections and (2) a layer-specific module that employs hypernetworks to generate tailored parameters for each layer. This dual design ensures an optimal balance between performance and parameter efficiency while introducing less than 0.05% additional parameters, making it significantly more compact than existing methods. Extensive experiments on diverse downstream tasks, few-shot settings and domain generalization demonstrate that SaS significantly enhances performance while maintaining superior parameter efficiency compared to existing methods, highlighting the importance of capturing both shared and layer-specific information in transfer learning. Code and data are available at https://anonymous.4open.science/r/SaS-PETL-3565.

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