Summary of Scale Generalisation Properties Of Extended Scale-covariant and Scale-invariant Gaussian Derivative Networks on Image Datasets with Spatial Scaling Variations, by Andrzej Perzanowski and Tony Lindeberg
Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations
by Andrzej Perzanowski, Tony Lindeberg
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 explores the scalability and adaptability of Gaussian derivative networks, a type of neural network designed to be covariant or invariant under spatial scaling transformations. The authors evaluate these networks on rescaled versions of Fashion-MNIST and CIFAR-10 datasets, as well as existing STIR datasets, showcasing their ability to generalize well across different scales. This research contributes to the understanding of scale generalisation properties in deep learning, with potential applications in image classification and computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special kind of neural network that can handle changes in size when looking at images. The researchers tested these networks on pictures from the Fashion-MNIST and CIFAR-10 datasets, but this time they were shrunk or zoomed in to see how well the networks could recognize what’s in the picture. They also used some existing datasets called STIR to compare their results with other types of neural networks. The findings show that these Gaussian derivative networks are really good at recognizing pictures even when the size changes, which is important for applications like image classification and computer vision. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Neural network
