mlsanityData to 14 Oct 2026

CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligence

arXiv 2106.05531 ↗First posted 10 Jun 2021eess.IV cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

8 of 18 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2019BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systemscited by 95
  4. 2018JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Servicescited by 80
  5. 2020Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analyticscited by 80
  6. 2019BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Servicescited by 68
  7. 2018Deep feature compression for collaborative object detectioncited by 48
  8. 2021Lightweight compression of neural network feature tensors for collaborative intelligencecited by 17

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1.

5 papers.

  1. 2021Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networkscited by 7
  2. 2023SplitFed resilience to packet loss: Where to split, that is the questioncited by 2
  3. 2024Optimizing Split Points for Error-Resilient SplitFed Learningcited by 1
  4. 2021DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channelscited by 0
  5. 2023Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligencecited by 0