Summary of Mixup Augmentation with Multiple Interpolations, by Lifeng Shen et al.
Mixup Augmentation with Multiple Interpolations
by Lifeng Shen, Jincheng Yu, Hansi Yang, James T. Kwok
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 an extension to Mixup, a popular class of data augmentation techniques. The new approach, called Multi-Mix, generates multiple interpolations from a sample pair, which can better guide the training process and reduce stochastic gradient variance. The authors demonstrate that Multi-Mix outperforms various Mixup variants and non-Mixup-based baselines in terms of generalization, robustness, and calibration on both synthetic and large-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer models better at learning from data. They use something called “Mixup” which makes the model see more examples by combining two things together. But they found that just doing this one time wasn’t enough, so they made it do it many times and call it “Multi-Mix”. It seems to work really well on lots of different kinds of data and helps the model make better predictions. |
Keywords
» Artificial intelligence » Data augmentation » Generalization
