Deep neural classifiers tend to rely on spurious correlations between
spurious attributes of inputs and targets to make predictions, which could
jeopardize their generalization capability. Training classifiers robust to
spurious correlations typically relies on annotations of spurious correlations
in data, which are often expensive to get. In this paper, we tackle an
annotation-free setting and propose a self-guided spurious correlation
mitigation framework. Our framework automatically constructs fine-grained
training labels tailored for a classifier obtained with empirical risk
minimization to improve its robustness against spurious correlations. The
fine-grained training labels are formulated with different prediction behaviors
of the classifier identified in a novel spuriousness embedding space. We
construct the space with automatically detected conceptual attributes and a
novel spuriousness metric which measures how likely a class-attribute
correlation is exploited for predictions. We demonstrate that training the
classifier to distinguish different prediction behaviors reduces its reliance
on spurious correlations without knowing them a priori and outperforms prior
methods on five real-world datasets.

在此研究中，我们提出了一种自引导的伪相关性缓解框架，它在无需标注伪相关性的情况下，自动构建了适合经验风险最小化得到的分类器的细粒度训练标签，以提高其对伪相关性的鲁棒性。通过在一种新颖的伪相关性嵌入空间中识别分类器的不同预测行为，并结合自动检测的概念属性和一种新颖的伪相关度量来构造细粒度的训练标签，我们证明了训练分类器以区分不同的预测行为可以减少其对伪相关性的依赖，而无需事先知道它们，并且在五个真实世界的数据集上优于先前的方法。