CapMatch:半监督的对比变压器囊,具有基于特征的知识蒸,用于识别人类活动.
IEEE transactions on neural networks and learning systems
|December 27, 2023
概括
CapMatch是一种新的半监督学习方法,通过结合监督和无监督学习来提高人类活动识别 (HAR). 它在HAR数据集上以最小的标记数据实现了高精度,超过了现有的方法.
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:
- 人类活动识别 (HAR) 对可穿戴技术至关重要.
- 现有的HAR半监督学习 (SSL) 方法可能很复杂.
- 需要使用有限的标记数据进行高效准确的HAR.
研究的目的:
- 提出CapMatch,一个简化的半监督的对比性囊变压器方法,用于HAR.
- 混合监督和无监督学习,以实现强大的特征提取.
- 通过基于特征的知识蒸 (KD) 提高 HAR 的性能.
主要方法:
- 开发了CapMatch,集成了伪标签,对比学习 (CL) 和基于特征的KD.
- 利用弱和时间切割数据增强来进行相似性学习.
- 设计了一个囊变压器网络,其中包含基于囊的变压器块和路由层.
主要成果:
- 在HAPT,WISDM和UCI_HAR数据集上,CapMatch的准确度达到了>85%,仅有10%的标记数据.
- 在低标签条件下超过了14个半监督算法.
- 达到≥88%的准确性与30%的标记数据,超过经典的监督算法,如决策树和KNN.
结论:
- CapMatch为HAR提供了一种简化但有效的SSL方法.
- 在从有限的标记数据中提取丰富的表示表现方面表现出卓越的性能.
- 突出了囊变压器网络在HAR任务中的潜力.
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