Summary of Neural Embedding Compression For Efficient Multi-task Earth Observation Modelling, by Carlos Gomes and Thomas Brunschwiler
Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling
by Carlos Gomes, Thomas Brunschwiler
First submitted to arxiv on: 26 Mar 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 This paper introduces Neural Embedding Compression (NEC), a novel method to reduce storage and transfer costs in earth observation (EO) data processing. By compressing embeddings rather than raw data, NEC achieves similar accuracy with a 75% to 90% reduction in data size. This is achieved by adapting foundation models through learned neural compression, requiring only a small fraction of the original parameters (10%) for a short training period (1% of pre-training iterations). The paper evaluates NEC on two EO tasks: scene classification and semantic segmentation. Compared to traditional compression methods, NEC maintains performance with minimal drops even at 99.7% compression rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a way to make big data smaller, so it’s easier to store and share. It does this by compressing the information that computers use to understand images of the Earth, rather than the images themselves. This helps reduce the amount of data needed to process and analyze these images. The new method is tested on two different tasks: identifying what types of landscapes are in a picture and finding specific objects within those pictures. It works well even when the data is greatly compressed. |
Keywords
» Artificial intelligence » Classification » Embedding » Semantic segmentation
