Summary of Cod: Learning Conditional Invariant Representation For Domain Adaptation Regression, by Hao-ran Yang et al.
COD: Learning Conditional Invariant Representation for Domain Adaptation Regression
by Hao-Ran Yang, Chuan-Xian Ren, You-Wei Luo
First submitted to arxiv on: 13 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 Domain Adaptation Regression (DAR) aims to transfer knowledge from a labeled source domain with continuous outputs to an unlabeled target domain. The existing conditional distribution alignment theory and methods, effective in classification settings, are not applicable due to the continuity problem in regression. This paper establishes the sufficiency theory for DAR, showing that generalization error can be dominated by cross-domain conditional discrepancy. A novel Conditional Operator Discrepancy (COD) is proposed, which admits metric properties on conditional distributions via kernel embedding theory. A COD-based conditional invariant representation learning model minimizes the discrepancy and improves discriminability. Theoretical results are verified through extensive experiments on standard DAR datasets, demonstrating superiority over state-of-the-art DAR methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer how to solve a new problem by showing it many examples from a related but different problem. This is called Domain Adaptation Regression (DAR). The big challenge here is that the new problem has continuous answers, like a number between 0 and 1. Existing methods that work well for other problems can’t be used because they’re designed for problems with simple yes/no answers. In this paper, researchers come up with a new way to solve DAR by creating a special “discrepancy” measure that helps the computer understand the difference between the two problems. They then use this measure to create a better model that can learn from examples and make accurate predictions. The results show that their method is more effective than other approaches. |
Keywords
* Artificial intelligence * Alignment * Classification * Domain adaptation * Embedding * Generalization * Regression * Representation learning
