Summary of Google2net: Going Transverse with Convolutions, by Yuanpeng He
GoogLe2Net: Going Transverse with Convolutions
by Yuanpeng He
First submitted to arxiv on: 1 Jan 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 CNN architecture called GoogLe2Net, which combines the benefits of residual connections and multi-scale features to improve performance in deep learning vision tasks. The architecture consists of ResFRI or Split-ResFRI modules that enable feature flow between adjacent layers and utilize residual connections to better process information. This allows for reutilization of captured features and expression of multi-scale features at a fine-grained level, leading to improved image classification performance. The authors demonstrate the effectiveness of GoogLe2Net on popular vision datasets such as CIFAR10, CIFAR100, and Tiny Imagenet, achieving better results than modern models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GoogLe2Net is a new way for computers to recognize images. It works by using old information it learned earlier to help it learn new things. This helps the computer make better decisions when classifying images. The new architecture also allows the computer to look at images from different angles, which makes it even more accurate. Scientists tested GoogLe2Net on lots of images and found that it was much better than other computers at recognizing what’s in pictures. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Image classification
