# Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme

arXiv: https://arxiv.org/abs/2302.13580 · HTML: https://mlsanity.com/paper/2302.13580/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [Nonlinear Transform Coding](https://mlsanity.com/paper/2007.03034/) (2020)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [Effects of Degradations on Deep Neural Network Architectures](https://mlsanity.com/paper/1807.10108/) (2018)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://mlsanity.com/paper/1809.01733/) (2018)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [DeepSIC: Deep Semantic Image Compression](https://mlsanity.com/paper/1801.09468/) (2018)
- [Recognition-Aware Learned Image Compression](https://mlsanity.com/paper/2202.00198/) (2022)

## Built on by (most cited first, 19 of 19)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2404.06448/) (2024)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework](https://mlsanity.com/paper/2409.13503/) (2024)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency](https://mlsanity.com/paper/2312.02984/) (2023)
- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion](https://mlsanity.com/paper/2401.01176/) (2024)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
- [Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding](https://mlsanity.com/paper/2505.10405/) (2025)
- [Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer](https://mlsanity.com/paper/2402.16868/) (2024)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication](https://mlsanity.com/paper/2508.17957/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things](https://mlsanity.com/paper/2409.12213/) (2024)
- [Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption](https://mlsanity.com/paper/2504.11873/) (2025)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [Adaptive Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2508.07958/) (2025)
- [Contextual Memory-Enhanced Source Coding for Low-SNR Communications](https://mlsanity.com/paper/2605.04400/) (2026)

## Abstract (arXiv)

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.