  In collaborative intelligence, an artificial intelligence (AI) model is
typically split between an edge device and the cloud. Feature tensors produced
by the edge sub-model are sent to the cloud via an imperfect communication
channel. At the cloud side, parts of the feature tensor may be missing due to
packet loss. In this paper we propose a method called Content-Adaptive Linear
Tensor Completion (CALTeC) to recover the missing feature data. The proposed
method is fast, data-adaptive, does not require pre-training, and produces
better results than existing methods for tensor data recovery in collaborative
intelligence.
