2023/09/05 by Eleonora Grassucci, Yuki Mitsufuji, Grassucci, Eleonora +5 · 1 voice
Computer Science · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #Computational and Text Analysis Methods #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Topic Modeling #cs.AI #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.02478
openalex publication_date 2023/09/05 · arxiv published 2023/09/05 · arxiv updated 2023/09/05 · openalex created_date 2023/09/09 · openalex updated_date 2026/07/28
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.