  Collaborative intelligence is a new paradigm for efficient deployment of deep
neural networks across the mobile-cloud infrastructure. By dividing the network
between the mobile and the cloud, it is possible to distribute the
computational workload such that the overall energy and/or latency of the
system is minimized. However, this necessitates sending deep feature data from
the mobile to the cloud in order to perform inference. In this work, we examine
the differences between the deep feature data and natural image data, and
propose a simple and effective near-lossless deep feature compressor. The
proposed method achieves up to 5% bit rate reduction compared to HEVC-Intra and
even more against other popular image codecs. Finally, we suggest an approach
for reconstructing the input image from compressed deep features in the cloud,
that could serve to supplement the inference performed by the deep model.
