Summary of Pac-fno: Parallel-structured All-component Fourier Neural Operators For Recognizing Low-quality Images, by Jinsung Jeon et al.
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
by Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong, Dongeun Lee, Kookjin Lee, Noseong Park
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel neural network model called parallel-structured and all-component Fourier neural operator (PAC-FNO) that can handle images of varying resolutions within a single model. Unlike conventional feed-forward neural networks, PAC-FNO operates in the frequency domain, allowing it to adapt to different image resolutions. The authors also introduce a two-stage algorithm for training PAC-FNO with minimal modifications to existing downstream models. They demonstrate the effectiveness of PAC-FNO by extensively evaluating it on seven image recognition benchmarks, showing that it improves the performance of baseline models by up to 77.1% when dealing with images with various natural variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer program called a neural network that can look at pictures and understand what’s in them. The problem is that these programs are not good at looking at pictures that are different sizes or have weird things like clouds or noise in them. To fix this, the researchers made a new type of neural network that can handle all kinds of pictures, no matter what size they are. They also came up with a way to train this new program using other programs that are already good at recognizing things. The results show that their new program is much better than the old ones and can even recognize things in pictures that have weird stuff like clouds or noise in them. |
Keywords
» Artificial intelligence » Neural network
