Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144189
Title: Lossy intermediate deep learning feature compression and evaluation
Authors: Chen, Zhuo
Fan, Kui
Wang, Shiqi
Duan, Ling-Yu
Lin, Weisi
Kot, Alex
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Chen, Z., Fan, K., Wang, S., Duan, L.-Y., Lin, W., & Kot, A. (2019). Lossy intermediate deep learning feature compression and evaluation. Proceedings of 27th ACM International Conference on Multimedia, 2414- 2422. doi:10.1145/3343031.3350849
Abstract: With the unprecedented success of deep learning in computer vision tasks, many cloud-based visual analysis applications are powered by deep learning models. However, the deep learning models are also characterized with high computational complexity and are task-specific, which may hinder the large-scale implementation of the conventional data communication paradigms. To enable a better balance among bandwidth usage, computational load and the generalization capability for cloud-end servers, we propose to compress and transmit intermediate deep learning features instead of visual signals and ultimately utilized features. The proposed strategy also provides a promising way for the standardization of deep feature coding. As the first attempt to this problem, we present a lossy compression framework and evaluation metrics for intermediate deep feature compression. Comprehensive experimental results show the effectiveness of our proposed methods and the feasibility of the proposed data transmission strategy. It is worth mentioning that the proposed compression framework and evaluation metrics have been adopted into the ongoing AVS (Audio Video Coding Standard Workgroup) - Visual Feature Coding Standard.
URI: https://hdl.handle.net/10356/144189
ISBN: 9781450368896
DOI: 10.1145/3343031.3350849
Rights: © 2019 Association for Computing Machinery (ACM). All rights reserved. This paper was published in 27th ACM International Conference on Multimedia and is made available with permission of Association for Computing Machinery (ACM).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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