走向统一的专业知识:从多样化的感知学习一个单一的视觉模型
IEEE transactions on pattern analysis and machine intelligence
|December 24, 2025
概括
模块化变压器模型Mod-Squad通过稀疏的专家激活来平衡任务合作和专业化. 这种方法可以实现高效的多数据集预培训和灵活的,资源高效的适应下游应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 多任务学习 (MTL) 面临来自冲突梯度的优化挑战.
- 在MTL中共享参数可能会阻碍特定任务的表示学习.
- 现有的大型模型往往优先考虑效率,而不是可调节的效率.
研究的目的:
- 引入Mod-Squad,这是MTL的模块化变压器模型,可以平衡合作和专业化.
- 将Mod-Squad扩展到跨异质来源的多数据集预训练.
- 开发高效的适应技术,以灵活微调模块化模型.
主要方法:
- 提出Mod-Squad,一个模块化变压器,每项任务激活少量专家子集.
- 引入了一种新的基于信息的相互损失,用于可差异匹配和统一异质数据.
- 开发了高效的适应技术,以动态调整模型大小,参数和计算成本.
主要成果:
- Mod-Squad有效地平衡了任务合作和专业化,避免了完全的骨干共享.
- 该模型通过增加任务和数据集大小来证明可扩展性.
- 通过混合适应方案实现了有利的性能效率权衡.
结论:
- Mod-Squad为稀疏的模块化模型提供了坚实的基础,能够从多样化的数据和监督中学习.
- 新兴的模块化促进了强大的概括,组件分解和高效的适应.
- 拟议的方法允许对下游任务的模型效率进行动态调整.
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