Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly prone to learning simple spurious features that are highly correlated with a label instead of the predictive but more complex core features. In this work, we show that, interestingly, the simplicity bias of gradient descent can be leveraged to identify spurious correlations, early in training. First, we prove on a two-layer neural network, that groups of examples with high spurious correlation are separable based on the model's output, in the initial training iterations. We further show that if spurious features have a small enough noise-to-signal ratio, the network's output on the majority of examples in a class will be almost exclusively determined by the spurious features and will be nearly invariant to the core feature. Finally, we propose SPARE, which separates large groups with spurious correlations early in training, and utilizes importance sampling to alleviate the spurious correlation, by balancing the group sizes. We show that SPARE achieves up to 5.6% higher worst-group accuracy than state-of-the-art methods, while being up to 12x faster. We also show the applicability of SPARE to discover and mitigate spurious correlations in Restricted ImageNet.

研究表明，梯度下降训练的神经网络具有归纳偏差，倾向于学习简单的解决方案，导致学习到与标签高度相关的简单虚假特征而非复杂的核心特征，此文介绍一种名为SPARE的方法，能够早期发现含有虚假相关性的大型分组，并利用重要性抽样来平衡组大小，从而减轻虚假关联的影响，相对于现有方法，SPARE方法的最差组准确度提高了最高达5.6%，速度提高了多达12倍。

通过简单偏好的视角早期识别训练中的伪偏差