代币级提示符混合与参数免费路由,用于联邦域名通用化
概括
通过使用令牌级提示组合和无参数路由来提高域泛化,以获得高效的专用模型. 这种方法提高了各种数据的性能,超过了以前的方法.
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:
- 联邦域泛化 (FedDG) 在分散的,异质的数据上训练模型.
- 现有的FedDG快速学习方法与样本多样性作斗争,导致性能下降.
- 专家混合 (MoE) 架构提供专业化,但在专家分配和通信成本方面面临挑战.
研究的目的:
- 引入TRIP,这是FEDDG的一个新型框架,它解决了目前基于MoE的快速学习的局限性.
- 通过令牌级专家分配来实现细粒度的视觉图案捕获.
- 通过无参数路由来减少通信上空成本.
主要方法:
- TRIP采用了一个标记级 pRompt 混合与无参数路由框架.
- 个别的图像令牌被分配给不同的提示专家,用于专门的学习.
- 无参数路由使用能力意识的集群和最佳运输 (OT) 进行高效的专家分配.
主要成果:
- 在四个基准指标中,TRIP实现了最佳的概括性能.
- 该框架大大降低了通信成本,只需要1K的参数.
- 标记级分配比图像级方法更有效地捕获细粒度的视觉模式.
结论:
- 特里普为联邦域名通用化提供了一种有效和高效的解决方案.
- 拟议的无参数路由机制大大减少了通讯开销.
- TRIP展示了令牌级专家分配对专业化和可泛化的模型的潜力.
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