An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication
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 compressi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4608 |
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| author | Shuhui Wang Zuxing Li Xin Huang Qi Jiang |
| author_facet | Shuhui Wang Zuxing Li Xin Huang Qi Jiang |
| author_sort | Shuhui Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aeb240bfd10d4f5c9f98fdf371d1ae0a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-aeb240bfd10d4f5c9f98fdf371d1ae0a2025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-04-01159460810.3390/app15094608An Explainable Deep Semantic Coding for Binary-Classification- Oriented CommunicationShuhui Wang0Zuxing Li1Xin Huang2Qi Jiang3College of Electronic and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai 201804, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai 201804, ChinaSemantic 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.https://www.mdpi.com/2076-3417/15/9/4608semantic communicationsemantic codingdeep learningvariational methodimage classification |
| spellingShingle | Shuhui Wang Zuxing Li Xin Huang Qi Jiang An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication Applied Sciences semantic communication semantic coding deep learning variational method image classification |
| title | An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication |
| title_full | An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication |
| title_fullStr | An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication |
| title_full_unstemmed | An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication |
| title_short | An Explainable Deep Semantic Coding for Binary-Classification- Oriented Communication |
| title_sort | explainable deep semantic coding for binary classification oriented communication |
| topic | semantic communication semantic coding deep learning variational method image classification |
| url | https://www.mdpi.com/2076-3417/15/9/4608 |
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