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 | Schools: | School of Computer Science and Engineering School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) |
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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