Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162594
Title: MobileDA: toward edge-domain adaptation
Authors: Yang, Jianfei
Zou, Han
Cao, Shuxin
Chen, Zhenghua
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Yang, J., Zou, H., Cao, S., Chen, Z. & Xie, L. (2020). MobileDA: toward edge-domain adaptation. IEEE Internet of Things Journal, 7(8), 6909-6918. https://dx.doi.org/10.1109/JIOT.2020.2976762
Project: RG72/19
Journal: IEEE Internet of Things Journal
Abstract: Deep neural networks (DNNs) have made significant advances in computer vision and sensor-based smart sensing. DNNs achieve prominent results based on standard data sets and powerful servers, whereas, in real applications with domain-shift data and resource-constrained environments such as Internet-of-Things (IoT) devices in the edge computing, DNNs are likely to have degraded performance in terms of accuracy and efficiency. To this end, we develop the MobileDA framework that learns transferable features while keeping the simple structure of the deep model. Our method allows a novel teacher network trained in the server to distill the knowledge for a student network running in the edge device, which is achieved by a cross-domain distillation. Leveraging unlabeled data in the new environment, our student model amends the feature learning to be domain invariant, then being our objective model running in the edge device. Our approach is evaluated on a challenging IoT-based WiFi gesture recognition scenario, and three classic visual adaptation benchmarks. The empirical studies corroborate the effectiveness of distillation for domain transfer, and the overall results show that our model achieves state-of-the-art performance merely using a simple network.
URI: https://hdl.handle.net/10356/162594
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.2976762
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2020.2976762.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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