Intermediate deep-feature compression for multitasking
Weiqian Wang, Ping An, Chao Yang, Xinpeng Huang · 2019
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.