Summary of Learning Tree-structured Composition Of Data Augmentation, by Dongyue Li et al.
Learning Tree-Structured Composition of Data Augmentation
by Dongyue Li, Kailai Chen, Predrag Radivojac, Hongyang R. Zhang
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Data Structures and Algorithms (cs.DS)
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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 The proposed approach leverages data augmentation techniques to improve neural network training with limited labeled data. The method relies on applying a sequence of multiple transformations to the input data, which enhances the robustness and generalizability of the model. Specifically, the technique employs advanced search methods, such as AutoAugment, to optimize over a set of predefined transformation sequences, rather than randomly sampling from a pre-selected list. This approach demonstrates improved performance on various benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation helps train neural networks even when there’s limited labeled data. To make this process better, scientists are trying new ways to apply multiple transformations to the data in sequence. They’re using advanced search methods to find the best combinations of transformations that work well together. This might lead to more accurate and reliable models. |
Keywords
» Artificial intelligence » Data augmentation » Neural network
