Summary of Lofi: Neural Local Fields For Scalable Image Reconstruction, by Amirehsan Khorashadizadeh et al.
LoFi: Neural Local Fields for Scalable Image Reconstruction
by AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu, Jason D. McEwen, Ivan Dokmanić
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel coordinate-based framework called LoFi (Local Field) is proposed for solving imaging inverse problems in computer vision. Unlike conventional methods, LoFi processes local information at each coordinate separately using multi-layer perceptrons (MLPs), allowing it to recover the object at that specific coordinate and achieving excellent generalization to out-of-distribution data. This approach requires significantly less memory than standard deep learning models like convolutional neural networks (CNNs) and vision transformers (ViTs). Additionally, LoFi’s local design enables training on extremely small datasets with 10 samples or fewer without overfitting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to fix problems in images called LoFi is invented. This method looks at pictures one piece at a time using special computer programs. It can make an image appear again even if it’s broken or blurry, and it does this really well! What’s cool is that LoFi uses very little memory, so it can work with big pictures without getting stuck. Plus, it can learn from tiny amounts of data, which means it’s super good at recognizing things. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Overfitting
