An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication
Shuhui Wang, Zuxing Li, Xin Huang, Qi Jiang · Applied Sciences · 2025
Semantic communication is emerging as a promising communication paradigm, where semantic coding plays an essential role by explicitly extracting task-critical information. Prior efforts toward semantic coding often rely on learning-based feature extraction methods but tend to overlook data compression and lack a rigorous theoretical foundation. To address these limitations, this paper proposes a novel explainable deep semantic coding framework, considering a binary mixed source and a classification task at the receiver. From an information-theoretic perspective, we formulate a semantic coding problem that jointly optimizes data compression rate and classification accuracy subject to distortion constraints. To solve this problem, we leverage deep learning techniques and variational approximation methods to develop practical deep semantic coding schemes. Experiments on the CelebA dataset and the CIFAR-10 dataset demonstrate that the proposed schemes effectively balance data compression and binary classification accuracy, which aligns with the theoretical formulation.