  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 of 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 layers. We also present the results for evaluation of
lossless deep feature compression with four benchmark data compression methods,
which provides meaningful investigations and baselines for future research and
standardization activities.
