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

arXiv: https://arxiv.org/abs/2309.02478 · HTML: https://mlsanity.com/paper/2309.02478/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 30 of 35)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [A 3D Generative Model for Structure-Based Drug Design](https://mlsanity.com/paper/2203.10446/) (2022)
- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization](https://mlsanity.com/paper/2205.07547/) (2022)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation](https://mlsanity.com/paper/2210.04296/) (2022)
- [Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis](https://mlsanity.com/paper/2205.04603/) (2022)
- [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022)
- [PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions](https://mlsanity.com/paper/2110.04176/) (2021)
- [Linear Semantics in Generative Adversarial Networks](https://mlsanity.com/paper/2104.00487/) (2021)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [Quaternion Generative Adversarial Networks](https://mlsanity.com/paper/2104.09630/) (2021)
- [SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer](https://mlsanity.com/paper/2301.12811/) (2023)
- [Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission](https://mlsanity.com/paper/2211.10287/) (2022)
- [YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction](https://mlsanity.com/paper/2306.14138/) (2023)
- [Hierarchical Diffusion Models for Singing Voice Neural Vocoder](https://mlsanity.com/paper/2210.07508/) (2022)
- [Wireless Deep Speech Semantic Transmission](https://mlsanity.com/paper/2211.02283/) (2022)
- [Enabling the Wireless Metaverse via Semantic Multiverse Communication](https://mlsanity.com/paper/2212.06908/) (2022)
- [Extracting Semantic Knowledge from GANs with Unsupervised Learning](https://mlsanity.com/paper/2211.16710/) (2022)

## Built on by (most cited first, 1 of 1)

- [Diffusion models for audio semantic communication](https://mlsanity.com/paper/2309.07195/) (2023)

## 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.