  Recent studies have shown that the efficiency of deep neural networks in
mobile applications can be significantly improved by distributing the
computational workload between the mobile device and the cloud. This paradigm,
termed collaborative intelligence, involves communicating feature data between
the mobile and the cloud. The efficiency of such approach can be further
improved by lossy compression of feature data, which has not been examined to
date. In this work we focus on collaborative object detection and study the
impact of both near-lossless and lossy compression of feature data on its
accuracy. We also propose a strategy for improving the accuracy under lossy
feature compression. Experiments indicate that using this strategy, the
communication overhead can be reduced by up to 70% without sacrificing
accuracy.
