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Model Stock: All we need is just a few fine-tuned models
March 29, 2024, 4:42 a.m. | Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from traditional practices that need a multitude of fine-tuned models for averaging, our approach employs significantly fewer models to achieve final weights yet yield superior accuracy. Drawing from key insights in the weight space of fine-tuned weights, we uncover a strong link between the performance and proximity to the center of weight space. Based …
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