Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels. Previous approaches based on worst-group loss minimization (e.g. Group-DRO) are effective in improving worse-group accuracy but require expensive group annotations for all the training samples. In this paper, we focus on the more challenging and realistic setting where group annotations are only available on a small validation set or are not available at all. We propose BAM, a novel two-stage training algorithm: in the first stage, the model is trained using a bias amplification scheme via introducing a learnable auxiliary variable for each training sample; in the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset. Empirically, BAM achieves competitive performance compared with existing methods evaluated on spurious correlation benchmarks in computer vision and natural language processing. Moreover, we find a simple stopping criterion based on minimum class accuracy difference that can remove the need for group annotations, with little or no loss in worst-group accuracy. We perform extensive analyses and ablations to verify the effectiveness and robustness of our algorithm in varying class and group imbalance ratios.

翻译：标准训练产生的神经网络已知在平均准确率较高的情况下，因某些虚假特征与标签之间的相关性，在稀有子群上的准确率较低。以往基于最差群体损失最小化（如Group-DRO）的方法可有效提升最差群体准确率，但需要所有训练样本的昂贵群体标注。本文聚焦于更具挑战性和现实性的场景：群体标注仅在小规模验证集上可用或完全不可用。我们提出BAM，一种新颖的两阶段训练算法：第一阶段，通过为每个训练样本引入可学习的辅助变量，采用偏差放大方案训练模型；第二阶段，对偏差放大模型误分类的样本进行权重提升，然后在重加权数据集上继续训练同一模型。实验表明，在计算机视觉和自然语言处理的虚假相关性基准测试中，BAM与现有方法相比取得了竞争性表现。此外，我们发现基于最小类别准确率差异的简单停止准则可消除对群体标注的需求，且最差群体准确率几乎无损。我们通过广泛分析和消融实验验证了算法在不同类别与群体不平衡比例下的有效性和鲁棒性。