Summary of Catch-up Mix: Catch-up Class For Struggling Filters in Cnn, by Minsoo Kang et al.
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
by Minsoo Kang, Minkoo Kang, Suhyun Kim
First submitted to arxiv on: 24 Jan 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 This paper proposes a novel approach to address over-reliance on strong filters in deep learning models for computer vision tasks. By introducing the Catch-up Mix method, which provides learning opportunities to a wide range of filters during training, the authors aim to promote more diverse representations and reduce reliance on a small subset of filters. The proposed method draws inspiration from image augmentation research that combats over-reliance on specific image regions by removing and replacing parts of images. Experimental results demonstrate the superiority of Catch-up Mix in various vision classification datasets, providing enhanced robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better computer vision models by not relying too much on just a few strong filters. It’s like when you’re learning something new, and some people are really good at it right away, but others might need more practice. The authors came up with an idea to help slow-learning filters catch up by mixing their activation maps with those of faster-learning filters. This helps the model learn in a more balanced way, so it’s not just relying on one or two strong filters. By doing this, the model becomes more robust and can handle small changes in the data better. |
Keywords
» Artificial intelligence » Classification » Deep learning
