Summary of Foundation Model For Lossy Compression Of Spatiotemporal Scientific Data, by Xiao Li and Jaemoon Lee and Anand Rangarajan and Sanjay Ranka
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
by Xiao Li, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a foundation model (FM) that combines variational autoencoder (VAE) with hyper-prior structure and super-resolution (SR) module to compress lossy scientific data. The VAE framework uses hyper-priors to model latent space dependencies, enhancing compression efficiency. The SR module refines low-resolution representations into high-resolution outputs, improving reconstruction quality. The proposed method efficiently captures spatiotemporal correlations in scientific data while maintaining low computational cost. Experimental results demonstrate that the FM generalizes well to unseen domains and varying data shapes, achieving up to 4 times higher compression ratios than state-of-the-art methods after domain-specific fine-tuning. Additionally, the SR module improves compression ratio by 30 percent compared to simple upsampling techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to shrink big scientific datasets without losing important details. The method combines two ideas: one that uses prior knowledge to make guesses about what’s in the data and another that refines these guesses to get more accurate results. This helps scientists compress their data efficiently while keeping it useful for analysis. The approach is tested on different types of data and shows great results, with some datasets shrinking by up to 4 times! The method also improves upon simple techniques used previously. |
Keywords
» Artificial intelligence » Fine tuning » Latent space » Spatiotemporal » Super resolution » Variational autoencoder
