Summary of Diffusion-driven Semantic Communication For Generative Models with Bandwidth Constraints, by Lei Guo et al.
Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints
by Lei Guo, Wei Chen, Yuxuan Sun, Bo Ai, Nikolaos Pappas, Tony Quek
First submitted to arxiv on: 26 Jul 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 The proposed diffusion-driven semantic communication framework combines advanced VAE-based compression with a generative model for bandwidth-constrained applications. The architecture utilizes a diffusion model, where the signal transmission process through the wireless channel serves as the forward process. To reduce bandwidth requirements, a downsampling module and paired upsampling module are incorporated based on a variational auto-encoder. A loss function is derived and evaluated through comprehensive experiments, demonstrating significant improvements in pixel-level metrics (PSNR) and semantic metrics (LPIPS). The enhancements are more pronounced at higher compression rates and signal-to-noise ratios compared to deep joint source-channel coding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re sending a message through a noisy phone line. To make sure it gets there okay, you need to compress the message so it takes up less space. This paper shows how to do that using a special kind of model called a diffusion model. It’s like a decoder ring that helps fix any mistakes made during transmission. They also tested their method and found that it works much better than some other approaches, especially when there’s a lot of noise or the message is really long. |
Keywords
* Artificial intelligence * Decoder * Diffusion * Diffusion model * Encoder * Generative model * Loss function
