Summary of Understanding Neural Network Systems For Image Analysis Using Vector Spaces and Inverse Maps, by Rebecca Pattichis and Marios S. Pattichis
Understanding Neural Network Systems for Image Analysis using Vector Spaces and Inverse Maps
by Rebecca Pattichis, Marios S. Pattichis
First submitted to arxiv on: 1 Feb 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 This research paper introduces novel techniques from Linear Algebra to model neural network layers as maps between signal spaces. The authors demonstrate how these mathematical methods can be used to visualize the weight spaces and convolutional layer kernels of complex neural networks, offering insights into their internal workings. Additionally, they show how residual vector spaces can be employed to visualize information lost at each layer, providing a deeper understanding of neural network behavior. Furthermore, the paper explores invertible networks using vector spaces for computing input images that yield specific outputs, showcasing its applications on two invertible networks and ResNet18. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers learn from pictures. The authors use math to show how different parts of a computer’s brain work together. They help us see what’s going on inside these neural networks by using special spaces to visualize the data flowing through them. This makes it easier to understand why certain images are recognized and classified. The paper also shows how we can use this knowledge to create new types of computers that can “undo” certain operations, allowing for more creative and flexible image analysis. |
Keywords
* Artificial intelligence * Neural network
