Summary of Freeaugment: Data Augmentation Search Across All Degrees Of Freedom, by Tom Bekor et al.
FreeAugment: Data Augmentation Search Across All Degrees of Freedom
by Tom Bekor, Niv Nayman, Lihi Zelnik-Manor
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 FreeAugment, a novel approach to automatic data augmentation search that simultaneously optimizes four degrees of freedom: the number of transformations, their types, order, and magnitudes. Unlike existing methods, FreeAugment uses a fully differentiable method to learn the optimal combination of transformations, avoiding redundant repetition while sampling. The authors demonstrate the effectiveness of this approach by achieving state-of-the-art results on various natural image benchmarks and beyond across other domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation is crucial for deep learning, as it improves generalization capabilities. However, finding the best image transformations can be time-consuming and requires expertise. FreeAugment is a new method that automates this process by optimizing all degrees of freedom simultaneously. It uses a differentiable approach to learn the number of transformations and their order, avoiding duplication. The authors test FreeAugment on several datasets and show it achieves better results than previous methods. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Generalization
