Summary of Cross-dataset Generalization in Deep Learning, by Xuyu Zhang et al.
Cross-Dataset Generalization in Deep Learning
by Xuyu Zhang, Haofan Huang, Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai, Honglin Liu
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optics (physics.optics)
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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 investigates the limitations of deep learning in imaging through scattering media, where it discovers that the mathematical relationships learned by the network are approximations dependent on the training dataset. The authors demonstrate that enhancing the diversity of the training dataset improves generalization across different datasets, as the mapping relationship of a linear physical model is independent of inputs. This study provides insights into designing training datasets to address the generalization issue in various deep learning-based applications, such as phase imaging, 3D imaging reconstruction, and laser speckle reduction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well artificial intelligence can image through foggy or messy light. They found that AI networks are not perfect because they learn from the data they’re trained on. If the training data is different from what it’s trying to predict, it might not work well. The researchers discovered a way to make the AI better by using more diverse training data. This can help solve problems in areas like medical imaging and self-driving cars. |
Keywords
* Artificial intelligence * Deep learning * Generalization
