计算机科学
生成语法
领域(数学)
人工智能
透视图(图形)
深度学习
生成模型
语义计算
会话(web分析)
自然语言处理
数据科学
语义网
万维网
数学
纯数学
作者
Eleonora Grassucci,Yuki Mitsufuji,Ping Zhang,Danilo Comminiello
标识
DOI:10.1109/icassp48485.2024.10448235
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI