Summary of Latent Diffusion Model-enabled Low-latency Semantic Communication in the Presence Of Semantic Ambiguities and Wireless Channel Noises, by Jianhua Pei et al.
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises
by Jianhua Pei, Cheng Feng, Ping Wang, Hina Tabassum, Dongyuan Shi
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel deep learning-based Semantic Communications system to maximize communication network efficiency. The proposed system addresses challenges from wireless channel uncertainties, source outliers, and poor generalization bottlenecks. Key contributions include developing an outlier-robust encoder using semantic errors from projected gradient descent, a lightweight latent space transformation adapter for one-shot learning, and an end-to-end consistency distillation strategy for deterministic denoising in noisy channels. The system demonstrates superiority across various datasets, exhibiting robustness to outliers, ability to transmit unknown distributions, and real-time channel denoising while preserving high human perceptual quality. Evaluation metrics include MS-SSIM and LPIPS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make communication networks work better by using special computer models. The problem is that these networks can get messed up if there’s noise or errors. To solve this, the researchers created a new system that helps correct these mistakes in real-time. They used three main ideas: making sure the data is accurate, adapting to changes quickly, and fixing noisy channels. This system worked really well across different tests, showing it can handle unexpected situations and preserve image quality. The results were measured using special metrics like MS-SSIM and LPIPS. |
Keywords
» Artificial intelligence » Deep learning » Distillation » Encoder » Generalization » Gradient descent » Latent space » One shot
