在域名独立WiFi-CSI手势识别预培训期间使用域名标签
Bram van Berlo1, Richard Verhoeven1, Nirvana Meratnia1
1Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究引入了对抗性域名分类,通过减少无监督预训练期间的域名转移来改善WiFi-CSI手势识别. 多标签域名分类有效地减少了域名位置和方向的变化.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 无监督表示学习对于WiFi-CSI手势识别至关重要,以尽量减少对标记数据的依赖.
- WiFi-CSI数据中的域因素会对培训前的表现产生负面影响,阻碍下游任务的准确性.
- 现有的方法很难在无监督的预培训阶段缓解领域转移的影响.
研究的目的:
- 在WiFi-CSI手势识别的预培训阶段提出和评估对抗性域分类的整合.
- 调查域名分类的多类和标签版本在减少域名影响方面的有效性.
- 在无监督表示学习过程中增强自动域发现.
主要方法:
- 实施了集成到无监督表示学习预培训阶段的对抗性领域分类器.
- 进行了广泛的交叉验证实验,使用Widar3和SignFi数据集与随机和离开域因子.
- 评估了单标签和多标签域名分类策略以及域名意识的无监督基线.
主要成果:
- 多标签域名分类显示,Widar3数据集上的位置 (1.2%的平均改善) 和方向 (0.4%的平均改善) 的域名转移减少了.
- 当负面观点组合在预训练期间跨越多个领域时,域移位减少受到负面影响.
- 视图对比性损失,当排斥不同的域视图时,在特征空间中加剧了域位移.
结论:
- 对抗性域名分类,特别是多标签,是缓解域名转移在WiFi-CSI手势识别预培训中的有效策略.
- 需要仔细考虑负视图采样,以防止在对比学习期间增加域移动.
- 拟议的方法推进了无监督学习,以使用WiFi-CSI数据进行强大的人机交互.
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