一个联合的车辆重新识别基准和性能优化,使用双相对比动态聚合.
Linhan Huang1, Jianqing Zhu1, Yutao Chen1
1College of Engineering, Huaqiao University, No. 269 Chenghua North Road, Quanzhou, 362021, Fujian, China.
概括
联合车辆重新识别 (FV-REID) 通过在分散的数据中训练模型来增强隐私. 一种新的双相对比动态聚合方法提高了FV-REID系统的性能和稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 智能运输系统 智能运输系统
背景情况:
- 车辆重新识别 (REID) 对智能运输至关重要,但由于集中数据处理,它引发了隐私问题.
- 现有的REID联合学习方法与数据异质性和模型可变性作斗争.
研究的目的:
- 引入一个用于联合车辆重新识别 (FV-REID) 的基准,具有多域数据集和评估协议.
- 提出一种新的联合学习聚合方法,以提高FV-REID的性能和稳定性.
主要方法:
- 开发了一个联合车辆重新识别 (FV-REID) 基准,包括一个多域数据集和基线联合平均方法 (FVVR).
- 提出了一种双相对比动态聚合 (DCDA) 方法,在联合训练期间动态调整模型聚合权重.
- 在早期,显著的模型变化期间,DCDA优先考虑较大的重量,并在较晚,更稳定的训练阶段优先考虑较小的重量.
主要成果:
- 与传统的REID模型相比,基线FVVR方法显示性能不足和高可变性,特别是在数据分布多样化的情况下.
- 拟议的DCDA方法显著提高了联合车辆重新识别的性能和稳定性.
- DCDA有效地解决了数据不平衡问题,使得拥有较小数据集的客户能够对全球模型做出有意义的贡献.
结论:
- 开发的FV-REID基准标准为评估保护隐私的车辆重新识别技术提供了一个标准化的平台.
- DCDA 方法为改善异质环境中的联合学习提供了强大的解决方案,特别是用于车辆重新识别.
- 这项工作通过更有效,更稳定的联合学习方法,推进了保护隐私的智能交通系统.
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