Summary of Optimal Layer Selection For Latent Data Augmentation, by Tomoumi Takase et al.
Optimal Layer Selection for Latent Data Augmentation
by Tomoumi Takase, Ryo Karakida
First submitted to arxiv on: 24 Aug 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 The paper explores the application of data augmentation (DA) to hidden layers in neural networks, a technique known as feature augmentation, which has been shown to improve performance. Previous studies have applied DA to specific layers without considering trends or optimal strategies. This study investigates the suitable layers for applying DA in various experimental configurations and proposes an adaptive layer selection (AdaLASE) method that updates the ratio of DA application for each layer based on gradient descent during training. The proposed method achieved high overall test accuracy on several image classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make neural networks better by changing the way data is added to hidden layers. Right now, people are just doing this randomly or only adding data to specific layers, but it’s not very systematic. So, the researchers in this study tried to figure out what makes sense and came up with a new method that adjusts how much data augmentation is used based on how well the network is learning. They tested it on some image recognition tasks and got good results. |
Keywords
» Artificial intelligence » Data augmentation » Gradient descent » Image classification
