Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications

2025-02-25

AI Summary

To address high semantic redundancy and low bandwidth efficiency in task-oriented communication, this paper proposes a goal-driven semantic compression and reconstruction framework that abandons conventional pixel-level reconstruction in favor of extracting task-critical semantics. Methodologically, it innovatively incorporates imitation learning into semantic communication, defining semantic fidelity via downstream task performance rather than perceptual metrics. We introduce the Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), which jointly achieves semantic-level vector quantization and task alignment. Built upon the VQ-VAE architecture, the framework integrates task-incentivized quality assessment and an imitation learning–driven semantic regeneration metric. Experimental results demonstrate that, at equal bitrates, our method reduces semantic distortion by 37% compared to baseline approaches, while significantly improving classification and object detection accuracy. These findings validate both the effectiveness and practicality of goal-oriented semantic representation for task-driven communication systems.

Technology Category

• Computer Vision: Representation Learning for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.

Problem

• Develops Goal-Oriented Semantic Variational Autoencoder
• Enhances semantic data compression efficiency
• Implements imitation learning for semantic relevance

Innovation

• Task-driven semantic quantization
• Goal-oriented semantic variational autoencoder
• Imitation learning for semantic reconstruction