EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Author:

Wang Liang,Zhang Nan,Qu Xiaoyang,Wang Jianzong,Wan Jiguang,Li Guokuan,Hu Kaiyu,Jiang Guilin,Xiao Jing

Publisher

Springer Nature Singapore

Reference34 articles.

1. Bhardwaj, R., et al.: Ekya: continuous learning of video analytics models on edge compute servers. In: 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2022), pp. 119–135 (2022)

2. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

3. Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. In: Ensemble Machine Learning, pp. 157–175. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4419-9326-7_5

4. Fang, T., Lu, N., Niu, G., Sugiyama, M.: Rethinking importance weighting for deep learning under distribution shift. Adv. Neural. Inf. Process. Syst. 33, 11996–12007 (2020)

5. Ghosh, A.M., Grolinger, K.: Edge-cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Trans. Ind. Inf. 17(3), 2191–2200 (2020)

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