A promising way to deploy Artificial Intelligence (AI)-based services on
mobile devices is to run a part of the AI model (a deep neural network) on the
mobile itself, and the rest in the cloud. This is sometimes referred to as
collaborative intelligence. In this framework, intermediate features from the
deep network need to be transmitted to the cloud for further processing. We
study the case where such features are used for multiple purposes in the cloud
(multi-tasking) and where they need to be compressible in order to allow
efficient transmission to the cloud. To this end, we introduce a new loss
function that encourages feature compressibility while improving system
performance on multiple tasks. Experimental results show that with the
compression-friendly loss, one can achieve around 20% bitrate reduction without
sacrificing the performance on several vision-related tasks.

在此研究中，作者介绍了一种新的损失函数，旨在促进 feature 的可压缩性，而不会牺牲多个任务的系统性能，作者称之为压缩友好的丢失函数，实验结果显示，使用这种丢失函数，可以实现约 20％的比特率降低。