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Updated: Sep 11, 2025

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持续无线行动识别与扩张压缩协调 (CAREC)
Tingting Zhang1,2, Qunhang Fu1, Han Ding3
1School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了CAREC,这是基于Wi-Fi的动作识别框架,可以防止在学习新动作时忘记旧动作. 在保持高精度的同时,CAREC有效地压缩模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现实世界的应用需要适应性系统,这些系统可以学习新的功能,而无需完全重新培训.
- 阶级增量学习 (CIL) 对不断发展的系统至关重要,但基于Wi-Fi的动作识别面临着灾难性遗忘和模型扩展等挑战.
- 现有的方法在增量学习场景中扎于性能退化和不受控制的参数增长.
研究的目的:
- 提出CAREC,一个基于Wi-Fi的室内动作识别的新型类增量框架.
- 为了解决灾难性遗忘和不受控制的模型扩展在CIL的行动识别.
- 为适应性和可扩展系统平衡动态模型扩展与高效压缩.
主要方法:
- 实施了多分支架构,以整合新的行动类,而不会降低现有类的性能.
- 使用平衡知识蒸来实现显著的模型压缩 (80%),同时保持准确性.
- 采用数据重播策略来保留旧的类信息和超级特征提取器来改善歧视.
主要成果:
- 在XRF55数据集上的四个增量学习阶段中,CAREC显示了51.82%的绩效下降.
- 仅用2108万个参数实现了67.84%的准确性,与传统方法相比,这意味着大幅减少.
- 该框架有效地压缩了模型,同时在增量学习中保持了强大的性能.
结论:
- 在基于Wi-Fi的动作识别中,CAREC为课堂增量学习提供了一个有效的解决方案.
- 拟议的框架成功地减轻了灾难性遗忘,并在增量更新期间管理模型大小.
- 通过CAREC,可以开发出更具适应性,可扩展性和高效的室内动作识别系统.
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