Model Stock: All we need is just a few fine-tuned models

• URL: http://arxiv.org/abs/2403.19522v1
• Date: Thu, 28 Mar 2024 15:57:20 GMT
• Title: Model Stock: All we need is just a few fine-tuned models
• Authors: Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han,
• Abstract summary: This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. We employ significantly fewer models to achieve final weights yet yield superior accuracy. We demonstrate the efficacy of Model Stock with fine-tuned models based upon pre-trained CLIP architectures.
• Score: 34.449901046895185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 on this, we introduce a method that approximates a center-close weight using only two fine-tuned models, applicable during or after training. Our innovative layer-wise weight averaging technique surpasses state-of-the-art model methods such as Model Soup, utilizing only two fine-tuned models. This strategy can be aptly coined Model Stock, highlighting its reliance on selecting a minimal number of models to draw a more optimized-averaged model. We demonstrate the efficacy of Model Stock with fine-tuned models based upon pre-trained CLIP architectures, achieving remarkable performance on both ID and OOD tasks on the standard benchmarks, all while barely bringing extra computational demands. Our code and pre-trained models are available at https://github.com/naver-ai/model-stock.

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