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

20 Sep 2023arXiv:2309.11127archive 2025-07-28

Hyelin Nam, Jihong Park, Jinho Choi, Mehdi Bennis, Seong-Lyun Kim

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.

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Tasks

Image GenerationIn-Context LearningKnowledge DistillationSemantic CommunicationText to Image GenerationText-to-Image Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text-to-Image Generation Flickr-8k SSC+SKD LPIPS 0.697 #1 of 1 Archive leaderboard report

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Methods

Knowledge Distillation

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