Spurious Correlations in Machine Learning: A Survey
Wenqian Ye Guangtao Zheng Xu Cao Yunsheng Ma Xia Hu Aidong Zhang
Machine learning systems are known to be sensitive to spurious correlations
between biased features of the inputs (e.g., background, texture, and secondary
objects) and the corresponding labels. These features and their correlations
with the labels are known as "spurious" because they tend to change with shifts
in real-world data distributions, which can negatively impact the model's
generalization and robustness. In this survey, we provide a comprehensive
review of this issue, along with a taxonomy of current state-of-the-art methods
for addressing spurious correlations in machine learning models. Additionally,
we summarize existing datasets, benchmarks, and metrics to aid future research.
The paper concludes with a discussion of the recent advancements and future
research challenges in this field, aiming to provide valuable insights for
researchers in the related domains.