Summary of Cvit: Continuous Vision Transformer For Operator Learning, by Sifan Wang et al.
CViT: Continuous Vision Transformer for Operator Learning
by Sifan Wang, Jacob H Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Continuous Vision Transformer (CViT) is a novel neural operator architecture that combines advances in computer vision to address challenges in learning complex physical systems. CViT uses a vision transformer encoder, grid-based coordinate embedding, and query-wise cross-attention mechanism to capture multi-scale dependencies. This allows for flexible output representations and consistent evaluation at arbitrary resolutions. The model achieves state-of-the-art performance on multiple benchmarks, often surpassing larger foundation models without extensive pretraining and roll-out fine-tuning. CViT exhibits robust handling of discontinuous solutions, multi-scale features, and intricate spatio-temporal dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CViT is a new way to build machine learning models that can understand complex physical systems. It uses ideas from computer vision to learn about these systems. The model is good at capturing different scales of information and can evaluate itself at any resolution. CViT does better than other models on many tasks, even without extra training or fine-tuning. This means it’s a big step forward in using machine learning for physical sciences. |
Keywords
» Artificial intelligence » Cross attention » Embedding » Encoder » Fine tuning » Machine learning » Pretraining » Vision transformer
