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.

本文研究了四种低秩张量完成方法的有效性，旨在恢复由深度学习模型产生的深度特征张量中的丢失数据，并研究了在各种情况下张量完成的有效性。

通过低秩张量完成实现错误容忍的协作智能