发表机构
School of Electronic Information, Central South University（中南大学电子信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对分布偏移下Deep JSCC的性能下降问题，提出结合类级对抗对齐与监督对比学习的领域自适应框架，在SVHN→MNIST任务上取得98.15%的目标域准确率，提升了目标域泛化性能。
AI 中文摘要
深度联合信源信道编码（Deep JSCC）通过将输入直接映射为信道符号和任务输出实现视觉语义传输，但在训练域与部署域间存在分布偏移时性能会下降。本文研究面向任务的Deep JSCC的单源领域自适应问题，提出分类-容量-不变性（CCI）函数，刻画可用信道容量和类条件跨域不变性如何影响目标域分类准确率。对源域最优解进行标量线性分析，并通过受控浅层非线性验证，发现目标域分类准确率沿不同控制路径（通过改变传输维度或信道信噪比CSNR获得）随不变性约束和可用容量呈非单调变化。随后提出领域自适应Deep JSCC框架，将基于伪标签的类级对抗对齐与置信度过滤后的目标样本上的监督对比学习相结合。在AWGN和瑞利衰落信道下的数字数据集和PACS数据集上的实验表明，该方法在不引入额外推理网络的情况下提升了目标域泛化性能。在SVHN→MNIST任务中，当CSNR为10 dB时，所提方法达到98.15%的目标域准确率。
英文摘要
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.
