Summary of Demonstrating the Efficacy Of Kolmogorov-arnold Networks in Vision Tasks, by Minjong Cheon
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks
by Minjong Cheon
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 study demonstrates the effectiveness of Kolmogorov-Arnold Networks (KAN) for vision tasks, such as image classification, by comparing its performance with state-of-the-art models like ResNet-18 and MLP-Mixer on MNIST, CIFAR10, and CIFAR100 datasets. The results show that KAN outperforms MLP-Mixer on CIFAR10 and CIFAR100 but performs slightly worse than ResNet-18. This suggests that KAN has significant promise for vision tasks, with potential for further enhancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kolmogorov-Arnold Networks (KAN) are a new type of deep learning model being tested for use in image classification tasks. The researchers tried using KAN on three different datasets and compared its performance to other popular models like ResNet-18 and MLP-Mixer. They found that KAN did well, especially on two of the datasets. This is exciting because it means KAN could be a useful tool for solving image recognition problems in the future. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Resnet
