让图像分类在不同域间传输更稳定，提升通信可靠性。
Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification
- 用伪标签和对比学习增强跨域特征对齐
- 在10dB CSNR下实现98.15%准确率
- 无需额外推理网络，适合实际部署
深度联合源信道编码（Deep JSCC）可直接将输入映射为信道符号与任务输出，但在训练与部署域分布偏移时性能下降。本文研究面向任务的单源域自适应问题，提出分类容量不变性（CCI）函数，刻画信道容量与类别条件跨域不变性对目标域分类精度的影响。标量线性分析与受控浅层非线性验证表明，目标域分类准确率随不变性约束和可用容量呈现非单调变化，沿改变传输维度或CSNR的路径可得不同控制效果。为此提出域自适应Deep JSCC框架，结合伪标签驱动的类别级对抗对齐与置信度筛选后目标样本的监督对比学习。在数字与PACS数据集上，针对加性高斯白噪声（AWGN）与瑞利衰落信道的实验表明，该方法在不引入额外推理网络的情况下显著提升目标域泛化能力。在SVHN→MNIST任务中，10 dB CSNR下达到98.15%准确率。
原文摘要 · Abstract (English)
Deep joint source--channel coding (Deep JSCC) enables visual semantic transmission by mapping inputs directly to channel symbols and task outputs, but its performance can deteriorate under distribution shifts between training and deployment domains. We study single-source domain adaptation for task-oriented Deep JSCC and formulate a classification-capacity-invariance (CCI) function to characterize how the available channel capacity and class-conditional cross-domain invariance affect target domain classification accuracy. A scalar linear analysis of source-domain-optimal solutions and a controlled shallow nonlinear validation show that target domain classification accuracy can vary non-monotonically with the invariance constraint and with available capacity along separate control paths obtained by varying the transmitted dimension or CSNR. We then propose a domain-adaptive Deep JSCC framework that combines pseudo-label-based class-level adversarial alignment with supervised contrastive learning on confidence-filtered target samples. Experiments on digit and PACS datasets over AWGN and Rayleigh fading channels demonstrate improved target domain generalization without introducing additional inference-time networks. On SVHN $\rightarrow$ MNIST, the proposed method achieves 98.15\% target-domain accuracy at a CSNR of 10 dB.
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