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In collaborative intelligence, an artificial intelligence (AI) model istypically split between an edge device and the cloud. Feature tensors producedby the edge sub-model are sent to the cloud via an imperfect communicationchannel. At the cloud side, parts of the feature tensor may be missing due topacket loss. In this paper we propose a method called Content-Adaptive LinearTensor Completion (CALTeC) to recover the missing feature data. The proposedmethod is fast, data-adaptive, does not require pre-training, and producesbetter results than existing methods for tensor data recovery in collaborativeintelligence.

CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligence