Summary of Data Augmentation with Automated Machine Learning: Approaches and Performance Comparison with Classical Data Augmentation Methods, by Alhassan Mumuni and Fuseini Mumuni
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
by Alhassan Mumuni, Fuseini Mumuni
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 Data augmentation is a crucial regularization technique in machine learning, used to improve model generalization performance. This survey focuses on automated machine learning (AutoML) principles for data augmentation, including data manipulation, integration, and synthesis techniques, with a focus on image data. The paper discusses search space design, hyperparameter optimization, and model evaluation methods, as well as an extensive comparison of AutoML-based methods to state-of-the-art conventional approaches. Results show that AutoML outperforms classical methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation is an important way to make machine learning models better. This paper looks at how we can use computers to help us find the best ways to do data augmentation, which is really helpful for making sure our models are good at guessing what they haven’t seen before. The computer helps with things like changing pictures or finding new information to add to a dataset. It also helps us figure out how well different methods work and which ones are the best. |
Keywords
* Artificial intelligence * Data augmentation * Generalization * Hyperparameter * Machine learning * Optimization * Regularization
