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Target-oriented Semi-supervised Domain Adaptation for WiFi-based HAR

IEEE Conference on Computer Communications (INFOCOM)(2022)CCF A

Univ Sci & Technol China | Univ Mississippi | Beijing Inst Technol | Tsinghua Univ | Xi An Jiao Tong Univ

Cited 12|Views45
Abstract
Incorporating domain adaptation is a promising solution to mitigate the domain shift problem of WiFi-based human activity recognition (HAR). The state-of-the-art solutions, however, do not fully exploit all the data, only focusing either on unlabeled samples or labeled samples in the target WiFi environment. Moreover, they largely fail to carefully consider the discrepancy between the source and target WiFi environments, making the adaptation of models to the target environment with few samples become much less effective. To cope with those issues, we propose a Target-Oriented Semi-Supervised (TOSS) domain adaptation method for WiFi-based HAR that can effectively leverage both labeled and unlabeled target samples. We further design a dynamic pseudo label strategy and an uncertainty-based selection method to learn the knowledge from both source and target environments. We implement TOSS with a typical meta learning model and conduct extensive evaluations. The results show that TOSS greatly outperforms state-of-the-art methods under comprehensive 1 on 1 and multi-source one-shot domain adaptation experiments across multiple real-world scenarios.
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Key words
TOSS,typical meta learning model,conduct extensive evaluations,multisource one-shot domain adaptation experiments,WiFi-based HAR,incorporating domain adaptation,domain shift problem,WiFi-based human activity recognition,unlabeled samples,labeled samples,target environment,labeled target samples,unlabeled target samples,dynamic pseudolabel strategy,uncertainty based selection method,target oriented semisupervised domain adaptation method
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Chat Paper

要点】:本文提出了一种针对基于WiFi的人体活动识别(HAR)的目标导向半监督域适应方法(TOSS),通过有效利用标记和未标记的目标样本,显著提高了模型在目标环境中的适应性。

方法】:本文采用了一种结合了标记和未标记样本的目标导向半监督学习方法。

实验】:研究者使用了一种典型的元学习模型来实现TOSS,并在多个现实场景中的一对一和多源一次性域适应实验中对TOSS进行了广泛评估。结果显示,在综合实验中,TOSS在性能上大幅超越了现有先进方法。