mlsanityData to 14 Oct 2026

Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview

arXiv 2309.02478 ↗First posted 5 Sep 2023cs.LG cs.AI eess.SPcited by 1

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Abstract (arXiv)

Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems. Its challenge of extracting semantic information from the original complex content and regenerating semantically consistent data at the receiver, possibly being robust to channel corruptions, can be addressed with deep generative models. This ICASSP special session overview paper discloses the semantic communication challenges from the machine learning perspective and unveils how deep generative models will significantly enhance semantic communication frameworks in dealing with real-world complex data, extracting and exploiting semantic information, and being robust to channel corruptions. Alongside establishing this emerging field, this paper charts novel research pathways for the next generative semantic communication frameworks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 5 Sep 2023.

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35 of 48 references.

References · 35

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23 more
  1. 2022SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantizationcited by 68
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  5. 2022FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equationcited by 36
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Diffusion models for audio semantic communicationcited by 17