无需预训练模型，自动融合多模型参数提升多任务性能
Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
- 通过动态排列参数并优化损失，实现无监督参数融合
- 在多个基准数据集上优于加权插值等现有方法
- 适合需要快速集成多模型的科研与工程场景
在深度学习快速发展的背景下，专用模型推动了计算机视觉和自然语言处理等任务的进步。然而，这种专业化导致模型生态碎片化，缺乏跨任务适应能力。为此，我们提出AutoFusion框架，可在无预训练检查点的情况下，融合相同架构的多个模型参数以支持多任务学习。该方法采用无监督、端到端策略，在每一层动态重排模型参数，通过损失最小化过程优化组合，无需标签数据。我们在常用基准数据集上验证了其有效性，表现优于权重插值、Git Re-Basin和ZipIt等现有方法。该框架为模型集成提供了可扩展、灵活的解决方案，适用于未来研究与实际应用。
原文摘要 · Abstract (English)
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.
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