Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
Keywords: Parameter Fusion, Multi-task Model Fusion, Computer Vision
TL;DR: An end-to-end multi-task model parameter fusion approach
Abstract: In the rapidly evolving field of deep learning, specialized models have driven significant advancements in tasks such as computer vision and natural language processing. However, this specialization leads to a fragmented ecosystem where models lack the adaptability for broader applications. To overcome this, we introduce AutoFusion, an innovative framework that integrates distinct models into a unified architecture for multi-task learning without pre-trained checkpoints. Using an unsupervised, end-to-end approach, AutoFusion dynamically blends model weights at each layer, optimizing the combination through a loss-minimization process that does not require labeled data. We validate AutoFusion’s effectiveness through experiments on commonly used benchmark datasets, demonstrating superior performance over established methods like Weight Interpolation, Git Re-Basin, and ZipIt. Our framework offers a scalable and flexible solution for model integration, positioning it as a powerful tool for future research and practical applications.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 5738
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