Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation

TL;DR

This paper introduces a language-oriented semantic communication framework for text-to-image generation, utilizing semantic coding and knowledge distillation to improve efficiency and robustness in machine communication.

Contribution

It presents three novel algorithms—semantic source coding, semantic channel coding, and semantic knowledge distillation—for enhancing semantic communication in text-to-image tasks.

Findings

Higher perceptual similarity with fewer transmissions

Improved robustness against noisy channels

Effective listener-customized prompt generation

Abstract

By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC). In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. To demonstrate LSC's potential, we introduce three innovative algorithms: 1) semantic source coding (SSC) which compresses a text prompt into its key head words capturing the prompt's syntactic essence while maintaining their appearance order to keep the prompt's context; 2) semantic channel coding (SCC) that improves robustness against errors by substituting head words with their lenghthier synonyms; and 3) semantic knowledge distillation (SKD) that produces listener-customized prompts via in-context learning the listener's language style. In a communication task for progressive text-to-image generation, the proposed methods achieve higher perceptual similarities with fewer transmissions while enhancing robustness in noisy communication channels.