Summary of Interpret Your Decision: Logical Reasoning Regularization For Generalization in Visual Classification, by Zhaorui Tan et al.
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification
by Zhaorui Tan, Xi Yang, Qiufeng Wang, Anh Nguyen, Kaizhu Huang
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 relationship between logical reasoning and deep learning generalization in visual classification. The authors introduce a logical regularization term, L-Reg, which bridges a logical analysis framework to image classification. L-Reg reduces model complexity by modifying feature distributions and classifier weights. This leads to improved interpretability, enabling the model to focus on salient features like faces for person classification. Theoretical analysis and experiments demonstrate that L-Reg enhances generalization across various scenarios, including multi-domain generalization and generalized category discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep learning models can improve their ability to classify images from new or unknown domains. They create a special type of regularization called L-Reg that helps the model make better decisions by simplifying its thinking. This makes it easier for humans to understand why the model is making certain predictions. The authors tested L-Reg and found that it works well in real-world situations where there are many different types of images. |
Keywords
» Artificial intelligence » Classification » Deep learning » Domain generalization » Generalization » Image classification » Regularization
