# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding
### The DVSC Model and Training Algorithm
Moreover, future work could explore adaptive optimization strategies that dynamically adjust the weight coefficient \(\mu\) based on task complexity, dataset characteristics, or real-time feedback mechanisms. In conclusion, this study introduces a deep semantic coding framework that is both explainable and mathematically principled, laying a solid foundation for future research in explainable semantic coding and efficient data compression strategies. Conceptualization, S.W. and Z.L. ; methodology, S.W. and Z.L. ; software, S.W. and Q.J. ; validation, S.W. ; formal analysis, S.W. ; investigation, Z.L. ; resources, Z.L. ; data curation, S.W. ; writing--original draft preparation, S.W. ; writing--review and editing, Z.L. and X.H. ; visualization, S.W. ; supervision, Z.L. ; project administration, Z.L; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the Inter-Governmental International Science and Technology Innovation Cooperation of National Key Research and Development Program of China (2023 YFE0112500) and the National Natural Science Foundation of China (62006173). Not applicable. The authors conducted experiments on two publicly available datasets: CelebA and CIFAR-10. These datasets can be accessed respectively at [[http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)]([http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)) (accessed on 22 January 2025) and [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 10 April 2025). ## References

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