Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual alignment method to detect and explore spurious correlations of black box classifiers. Counterfactual images generated with respect to one classifier can be input into other classifiers to see if they also induce changes in the outputs of these classifiers. The relationship between these responses can be quantified and used to identify specific instances where a spurious correlation exists as well as compute aggregate statistics over a dataset. Our work demonstrates the ability to detect spurious correlations in face attribute classifiers. This is validated by observing intuitive trends in a face attribute classifier as well as fabricating spurious correlations and detecting their presence, both visually and quantitatively. Further, utilizing the CF alignment method, we demonstrate that we can rectify spurious correlations identified in classifiers.

通过计算输入不同分类器后输出的响应之间的关系，我们提出了反事实对齐方法来检测和探索黑盒分类器中的虚假相关性，并验证了在人脸属性分类器中检测到虚假相关性的能力，同时证明了可以通过CF对齐方法纠正分类器中检测到的虚假相关性。

利用反事实对齐方法识别虚假相关性