Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
<|reference_start|>Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion: 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 fuses distinct model parameters(with the same architecture) for multi-task learning without pre-trained checkpoints. Using an unsupervised, end-to-end approach, AutoFusion dynamically permutes model parameters 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.<|reference_end|>
@article{tian2024wolf2pack:,
    title={Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion},
    author={Bowen Tian, Songning Lai, Yutao Yue},
    journal={arXiv preprint arXiv:2410.05746},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05746},
    primaryClass={cs.CV cs.LG}
}