Summary of Residual Feature-reutilization Inception Network For Image Classification, by Yuanpeng He et al.
Residual Feature-Reutilization Inception Network for Image Classification
by Yuanpeng He, Wenjie Song, Lijian Li, Tianxiang Zhan, Wenpin Jiao
First submitted to arxiv on: 27 Dec 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 The paper proposes a novel CNN architecture, ResFRI or Split-ResFRI, which incorporates residual feature-reutilization inceptions to effectively extract multi-scale feature information. The design consists of four convolutional combinations with specially designed information interaction passages, allowing for the adjustment of segmentation ratios and reducing the number of parameters while maintaining performance. Experimental results on popular vision datasets such as CIFAR10, CIFAR100, and Tiny Imagenet demonstrate state-of-the-art results compared to modern models of similar size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to design CNNs that helps them learn more features from images. It’s like giving the model a special tool that lets it re-use information in different parts of the image. This tool is called ResFRI or Split-ResFRI, and it’s made up of four parts that work together to help the model see things at different scales. The design also allows for adjusting how much information is used from each part, which helps reduce the number of parameters needed while keeping performance good. The paper shows that this new approach works well on popular image datasets. |
Keywords
» Artificial intelligence » Cnn
