Computer vision datasets frequently contain spurious correlations between
task-relevant labels and (easy to learn) latent task-irrelevant attributes
(e.g. context). Models trained on such datasets learn "shortcuts" and
underperform on bias-conflicting slices of data where the correlation does not
hold. In this work, we study the problem of identifying such slices to inform
downstream bias mitigation strategies. We propose First Amplify Correlations
and Then Slice to Discover Bias (FACTS), wherein we first amplify correlations
to fit a simple bias-aligned hypothesis via strongly regularized empirical risk
minimization. Next, we perform correlation-aware slicing via mixture modeling
in bias-aligned feature space to discover underperforming data slices that
capture distinct correlations. Despite its simplicity, our method considerably
improves over prior work (by as much as 35% precision@10) in correlation bias
identification across a range of diverse evaluation settings. Our code is
available at: this https URL

通过放大相关性以及切分来发现偏见，我们的 FACTS 方法在不同的评估设置中显著改善了与相关性偏见识别的前期工作相比，接近 35% 的 10 项精确度。