Intermediate deep-feature compression for multitasking

Weiqian Wang; Ping An; Chao Yang; Xinpeng Huang

doi:10.1117/12.2538738

18 November 2019 Intermediate deep-feature compression for multitasking

Weiqian Wang, Ping An, Chao Yang, Xinpeng Huang

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 111870Z (2019) https://doi.org/10.1117/12.2538738
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

Collaborative intelligence is a new strategy to deploy deep neural network model for AI-based mobile devices, which runs a part of model on the mobile to extract features, the rest part in the cloud. In such case, feature data but not the raw image needs to be transmitted to cloud, and the features uploaded to cloud need have generalization capability to complete multitask. To this end, we design an encoder-decoder network to get intermediate deep features of image, and propose a method to make the features complete different tasks. Finally, we use a lossy compression method for intermediate deep features to improve transmission efficiency. Experimental results show that the features extracted by our network can complete input reconstruction and object detection simultaneously. Besides, with the deep-feature compression method proposed in our work, the quality of reconstructed image is good in visual and index of quantitative assessment, and object detection also has a good result in accuracy.

Citation Download Citation

Weiqian Wang, Ping An, Chao Yang, and Xinpeng Huang "Intermediate deep-feature compression for multitasking", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111870Z (18 November 2019); https://doi.org/10.1117/12.2538738

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KEYWORDS

Computer programming

Clouds

Feature extraction

Image quality

Instrument modeling

Network architectures

Visual process modeling

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