Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification
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-mo...
| Authors | Yishen Li, Xuechen Chen, Xiaoheng Deng et al. |
|---|---|
| Primary category | cs.IT |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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