Summary of Towards Combating Frequency Simplicity-biased Learning For Domain Generalization, by Xilin He et al.
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
by Xilin He, Jingyu Hu, Qinliang Lin, Cheng Luo, Weicheng Xie, Siyang Song, Muhammad Haris Khan, Linlin Shen
First submitted to arxiv on: 21 Oct 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 proposes two data augmentation modules to mitigate the simplicity-biased learning behavior of neural networks, which leads to over-reliance on frequency shortcuts and poor generalization performance. The authors argue that changing the statistical structure of the dataset in the Fourier domain can manipulate the learning behavior on different frequency components. They design two modules to collaboratively adjust the frequency characteristic of the dataset, influencing the model’s learning behavior and preventing shortcut learning. The proposed approach aims to improve domain generalization by enhancing transferable knowledge from source domains. The authors provide code for their method at AdvFrequency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to help artificial intelligence (AI) learn better from some data so it can work well on new, unseen data too. Sometimes AI models get stuck learning specific patterns in the data and don’t use more important information. The researchers want to fix this by changing the way the data is structured. They came up with two ways to do this, which helps the model learn better without getting stuck. |
Keywords
» Artificial intelligence » Data augmentation » Domain generalization » Generalization
