Summary of Rethinking Multi-domain Generalization with a General Learning Objective, by Zhaorui Tan et al.
Rethinking Multi-domain Generalization with A General Learning Objective
by Zhaorui Tan, Xi Yang, Kaizhu Huang
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a new framework for multi-domain generalization (mDG) by rethinking the learning objective and relaxing constraints on target marginal distributions. The authors introduce a Y-mapping to facilitate domain-independent conditional feature learning and posterior maximization. They also develop two regularization terms to incorporate prior information and suppress invalid causality, addressing issues with relaxed constraints. Theoretical contributions include an upper bound for domain alignment, highlighting that many previous mDG approaches only optimize part of the objective, leading to limited performance. The proposed framework is demonstrated to improve mDG performance in various downstream tasks, including regression, segmentation, and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines can learn from different sources of information. Right now, it’s hard for machines to make good predictions when the data they’re trained on is very different from what they’re trying to predict. The authors came up with a new way to help machines learn from this kind of data by changing the way we think about learning objectives. They also developed some new techniques to make sure the machine doesn’t get stuck in its ways and can adapt to new information. This could be really important for things like self-driving cars, medical diagnosis, or predicting the weather. |
Keywords
* Artificial intelligence * Alignment * Classification * Domain generalization * Regression * Regularization
