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arXiv 2304.09438cs.ITmath.IT
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基于对比学习的无线图像传输语义通信

Contrastive Learning based Semantic Communication for Wireless Image Transmission

Shunpu Tang, Qianqian Yang, Lisheng Fan, Xianfu Lei, Yansha Deng, Arumugam Nallanathan

AI总结:

本文提出基于对比学习的语义通信方法,将传输损坏视为数据增强,通过两阶段训练联合优化语义编解码器,在CIFAR10上实现最高56%的识别准确率提升。

摘要:

近年来,语义通信在无线图像传输系统中得到了广泛应用,因为它能够优先保留图像中有意义的语义信息,而非传输符号的准确性,从而提升通信效率。然而,现有的语义通信方法在实现下游AI任务(如图像识别)中可观的推理性能,或在推理性能与接收端重建图像质量之间取得平衡方面,仍然面临局限。因此,本文提出了一种基于对比学习(CL)的语义通信方法来克服这些局限。具体而言,我们将传输过程中的图像损坏视为CL中的一种数据增强形式,并利用CL缩小原始图像与损坏重建图像之间的语义距离,同时保持不相关图像之间的语义距离,以便在下游任务中获得更好的判别能力。此外,我们设计了一个两阶段训练过程及相应的损失函数,用于联合优化语义编码器和解码器,从而在下游图像识别任务的性能与重建质量之间实现良好的权衡。最后,通过仿真验证了所提方法相较于竞争方法的优越性。特别是,当带宽压缩比为1/48时,所提方法在CIFAR10数据集上可实现高达56%的准确率提升。

英文摘要:

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56\% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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