# Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

arXiv: https://arxiv.org/abs/2205.00271 · HTML: https://mlsanity.com/paper/2205.00271/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 68 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1803.10081/) (2018)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Beyond Sharing Weights for Deep Domain Adaptation](https://mlsanity.com/paper/1603.06432/) (2016)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2110.06530/) (2021)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [A Variational Auto-Encoder Model for Stochastic Point Processes](https://mlsanity.com/paper/1904.03273/) (2019)
- [Pixel-Level Domain Transfer](https://mlsanity.com/paper/1603.07442/) (2016)
- [A new communication paradigm: from bit accuracy to semantic fidelity](https://mlsanity.com/paper/2101.12649/) (2021)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)

## Built on by (most cited first, 30 of 36)

- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning](https://mlsanity.com/paper/2210.16118/) (2022)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises](https://mlsanity.com/paper/2406.06644/) (2024)
- [Visual Language Model based Cross-modal Semantic Communication Systems](https://mlsanity.com/paper/2407.00020/) (2024)
- [Knowledge Base Enabled Semantic Communication: A Generative Perspective](https://mlsanity.com/paper/2311.12443/) (2023)
- [Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks](https://mlsanity.com/paper/2212.10438/) (2022)
- [Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications](https://mlsanity.com/paper/2310.07987/) (2023)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Agent-driven Generative Semantic Communication with Cross-Modality and Prediction](https://mlsanity.com/paper/2404.06997/) (2024)
- [Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck](https://mlsanity.com/paper/2405.09514/) (2024)
- [Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems](https://mlsanity.com/paper/2208.05704/) (2022)
- [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing](https://mlsanity.com/paper/2411.15702/) (2024)
- [Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels](https://mlsanity.com/paper/2406.10556/) (2024)
- [Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks](https://mlsanity.com/paper/2406.18741/) (2024)
- [Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks](https://mlsanity.com/paper/2505.17030/) (2025)
- [Contrastive Language-Image Pre-Training Model based Semantic Communication Performance Optimization](https://mlsanity.com/paper/2507.08873/) (2025)
- [Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment](https://mlsanity.com/paper/2412.00862/) (2024)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning](https://mlsanity.com/paper/2505.19940/) (2025)
- [Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images](https://mlsanity.com/paper/2506.19893/) (2025)
- [EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision](https://mlsanity.com/paper/2210.13047/) (2022)
- [Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel](https://mlsanity.com/paper/2410.12302/) (2024)

## Abstract (arXiv)

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.