# Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

arXiv: https://arxiv.org/abs/2304.02317 · HTML: https://mlsanity.com/paper/2304.02317/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2023.

## Built on by (most cited first, 18 of 18)

- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [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)
- [Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search](https://mlsanity.com/paper/2506.19451/) (2025)
- [MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model](https://mlsanity.com/paper/2409.16592/) (2024)
- [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (2025)
- [FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding](https://mlsanity.com/paper/2601.00644/) (2026)
- [Federated Contrastive Learning for Personalized Semantic Communication](https://mlsanity.com/paper/2406.09182/) (2024)
- [NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission](https://mlsanity.com/paper/2502.19873/) (2025)
- [Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack](https://mlsanity.com/paper/2506.19886/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Quantization-Aware Collaborative Inference for Large Embodied AI Models](https://mlsanity.com/paper/2602.13052/) (2026)
- [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024)
- [Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)](https://mlsanity.com/paper/2409.16594/) (2024)
- [A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction](https://mlsanity.com/paper/2503.23866/) (2025)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding](https://mlsanity.com/paper/2508.07722/) (2025)
- [When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization](https://mlsanity.com/paper/2605.05514/) (2026)

## Abstract (arXiv)

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes.