Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143979
Title: Toward intelligent sensing : intermediate deep feature compression
Authors: Chen, Zhuo
Fan, Kui
Wang, Shiqi
Duan, Lingyu
Lin, Weisi
Kot, Alex Chichung
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Chen, Z., Fan, K., Wang, S., Duan, L., Lin, W., & Kot, A. C. (2019). Toward intelligent sensing : intermediate deep feature compression. IEEE Transactions on Image Processing, 29, 2230-2243. doi:10.1109/TIP.2019.2941660
Journal: IEEE Transactions on Image Processing
Abstract: The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features with high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layer features. We also present the results for evaluations of both lossless and lossy deep feature compression, which provide meaningful investigations and baselines for future research and standardization activities.
URI: https://hdl.handle.net/10356/143979
ISSN: 1057-7149
DOI: 10.1109/TIP.2019.2941660
Rights: © 2019 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/TIP.2019.2941660
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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