  In the race to bring Artificial Intelligence (AI) to the edge, collaborative
intelligence has emerged as a promising way to lighten the computation load on
edge devices that run applications based on Deep Neural Networks (DNNs).
Typically, a deep model is split at a certain layer into edge and cloud
sub-models. The deep feature tensor produced by the edge sub-model is
transmitted to the cloud, where the remaining computationally intensive
workload is performed by the cloud sub-model. The communication channel between
the edge and cloud is imperfect, which will result in missing data in the deep
feature tensor received at the cloud side. In this study, we examine the
effectiveness of four low-rank tensor completion methods in recovering missing
data in the deep feature tensor. We consider both sparse tensors, such as those
produced by the VGG16 model, as well as non-sparse tensors, such as those
produced by ResNet34 model. We study tensor completion effectiveness in both
conplexity-constrained and unconstrained scenario.
