Summary of A Survey on Mixup Augmentations and Beyond, by Xin Jin et al.
A Survey on Mixup Augmentations and Beyond
by Xin Jin, Hongyu Zhu, Siyuan Li, Zedong Wang, Zicheng Liu, Chang Yu, Huafeng Qin, Stan Z. Li
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 presents a comprehensive review of foundational mixup methods and their applications in deep neural networks. The authors focus on data augmentations, which have gained attention as regularization techniques when massive labeled data are unavailable. Specifically, they explore Mixup and related data-mixing methods that combine selected samples and labels to generate virtual data. These methods have achieved high performances by easily migrating to various domains. The paper elaborates on the training pipeline with mixup augmentations as a unified framework containing modules. Applications of mixup augmentations are investigated on vision downstream tasks, various data modalities, and some analysis and theorems of mixup. The authors conclude the current status and limitations of mixup research, pointing out further work for effective and efficient mixup augmentations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to make training artificial intelligence (AI) models better by adding fake data to the real data they learn from. This is called “mixup” and it helps AI models generalize to new situations more easily. The authors of this paper want to help people understand how mixup works and what it can be used for. They explain how to use mixup to improve AI model performance on different tasks, like recognizing objects in pictures or understanding speech. Mixup is an important part of making sure AI models are useful in the real world. |
Keywords
» Artificial intelligence » Attention » Regularization
