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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Contrastive Learning based Semantic Communication for Wireless Image Transmission


    Contributors:


    Publication date :

    2023-10-10


    Size :

    2619781 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    OTCLDA: Optimal Transport and Contrastive Learning for Domain Adaptive Semantic Segmentation

    Fan, Qizhe / Shen, Xiaoqin / Ying, Shihui et al. | IEEE | 2024


    Semantic Communication Approach for Multi-Task Image Transmission

    Zhang, Zhenguo / Yang, Qianqian / He, Shibo et al. | IEEE | 2022