Summary of Learning Robust Classifiers with Self-guided Spurious Correlation Mitigation, by Guangtao Zheng et al.
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
by Guangtao Zheng, Wenqian Ye, Aidong Zhang
First submitted to arxiv on: 6 May 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 The proposed framework tackles the issue of deep neural classifiers relying on spurious correlations, which can impact their generalization capabilities. By automatically constructing fine-grained training labels tailored to a classifier’s prediction behaviors, the framework improves its robustness against such correlations without requiring annotations. This is achieved through a novel spuriousness embedding space that detects conceptual attributes and measures the likelihood of class-attribute correlations being exploited for predictions. The framework outperforms prior methods on five real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer programs better at predicting things, like what’s in a picture or what someone said. Right now, these programs can get fooled by tiny details that don’t really matter. To fix this, the researchers came up with a new way to teach the program without needing extra information about what those tiny details are. They did this by making the program learn how it makes predictions differently in different situations. This helps the program focus on the important things and ignore the unimportant ones. It works really well and beats other ways of doing it. |
Keywords
» Artificial intelligence » Embedding space » Generalization » Likelihood
