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DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels

arXiv 2112.00794 ↗First posted 1 Dec 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

In edge-cloud collaborative intelligence (CI), an unreliable transmission channel exists in the information path of the AI model performing the inference. It is important to be able to simulate the performance of the CI system across an imperfect channel in order to understand system behavior and develop appropriate error control strategies. In this paper we present a simulation framework called DFTS2, which enables researchers to define the components of the CI system in TensorFlow~2, select a packet-based channel model with various parameters, and simulate system behavior under various channel conditions and error/loss control strategies. Using DFTS2, we also present the most comprehensive study to date of the packet loss concealment methods for collaborative image classification models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 1 Dec 2021.

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References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  5. 2019BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systemscited by 95
  6. 2018JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Servicescited by 80
  7. 2018Deep feature compression for collaborative object detectioncited by 48
  8. 2019Multi-task learning with compressible features for Collaborative Intelligencecited by 20
  9. 2021Collaborative Intelligence: Challenges and Opportunitiescited by 19
  10. 2018Near-Lossless Deep Feature Compression for Collaborative Intelligencecited by 17
  11. 2020Pareto-Optimal Bit Allocation for Collaborative Intelligencecited by 17
  12. 2021Lightweight compression of neural network feature tensors for collaborative intelligencecited by 17
3 more
  1. 2020Back-and-Forth prediction for deep tensor compressioncited by 16
  2. 2021Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligencecited by 7
  3. 2021CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligencecited by 5

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