Summary of Revisiting Spurious Correlation in Domain Generalization, by Bin Qin et al.
Revisiting Spurious Correlation in Domain Generalization
by Bin Qin, Jiangmeng Li, Yi Li, Xuesong Wu, Yupeng Wang, Wenwen Qiang, Jianwen Cao
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles the issue of models learning spurious correlations in out-of-distribution (OOD) scenarios, which hinders their generalization abilities. Recent works have proposed structural causal models (SCMs) to describe causality within data generation processes, thereby avoiding the learning of spurious correlations. However, from a machine learning perspective, these SCM-based analyses overlook the difference between data generation and representation learning processes, making them less effective for adapting to the latter. To address this, the authors propose building an SCM for representation learning processes and conducting a thorough analysis of spurious correlation mechanisms. They highlight the importance of selecting correct spurious correlation mechanisms based on practical application scenarios and demonstrate the effectiveness of their approach using synthetic and large-scale real OOD datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is trying to make machine learning models better at understanding data from new, unfamiliar situations. Right now, these models can learn fake patterns in the data that don’t actually mean anything. The authors are proposing a new way to analyze how data is generated so that models can avoid learning these useless patterns. They’re also suggesting a special tool called a propensity score weighted estimator that can help control for any mistakes the model makes when it tries to understand this unfamiliar data. Overall, their approach seems to be really good at helping machine learning models generalize better in new situations. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Representation learning
