Authors

* External authors

Venue

Date

Share

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

Eleonora Grassucci*

Yuki Mitsufuji

Ping Zhang*

Danilo Comminiello*

* External authors

ICASSP 2024

2023

Abstract

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.

Related Publications

PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher

NeurIPS, 2024
Dongjun Kim*, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon*

To accelerate sampling, diffusion models (DMs) are often distilled into generators that directly map noise to data in a single step. In this approach, the resolution of the generator is fundamentally limited by that of the teacher DM. To overcome this limitation, we propose …

GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping

NeurIPS, 2024
Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira, Naoki Murata, Shoukang Hu, Chieh-Hsin Lai, Seungryong Kim*, Yuki Mitsufuji

Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth e…

The whole is greater than the sum of its parts: improving music source separation by bridging networks

EURASIP, 2024
Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich*, Shusuke Takahashi*, Yuki Mitsufuji

This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation…

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

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.