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.

基于引入可学习的辅助变量的 BAM 算法通过扩大偏差，训练模型并在重新加权数据集上继续训练，从而在计算机视觉和自然语言处理的虚假相关基准测试中取得了竞争性的性能，同时发现基于最小类别准确率差异的简单停止准则可以消除对群组注释的需求，或者在最差组准确率上几乎没有损失。

偏见放大提升少数群体表现

Bias Amplification Enhances Minority Group Performance

The paradigm of worst-group loss minimization has shown its promise in
avoiding to learn spurious correlations, but requires costly additional
supervision on spurious attributes. To resolve this, recent works focus on
developing weaker forms of supervision -- e.g., hyperparameters discovered with
a small number of validation samples with spurious attribute annotation -- but
none of the methods retain comparable performance to methods using full
supervision on the spurious attribute. In this paper, instead of searching for
weaker supervisions, we ask: Given access to a fixed number of samples with
spurious attribute annotations, what is the best achievable worst-group loss if
we "fully exploit" them? To this end, we propose a pseudo-attribute-based
algorithm, coined Spread Spurious Attribute (SSA), for improving the
worst-group accuracy. In particular, we leverage samples both with and without
spurious attribute annotations to train a model to predict the spurious
attribute, then use the pseudo-attribute predicted by the trained model as
supervision on the spurious attribute to train a new robust model having
minimal worst-group loss. Our experiments on various benchmark datasets show
that our algorithm consistently outperforms the baseline methods using the same
number of validation samples with spurious attribute annotations. We also
demonstrate that the proposed SSA can achieve comparable performances to
methods using full (100%) spurious attribute supervision, by using a much
smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on
the dataset.

本文提出了一种名为 SSA 的伪属性增强算法，通过利用带有和不带有虚假属性注释的样本来预测虚假属性并最小化最差组损失来实现对噪声数据的高效识别和分类，同时证明了 SSA 算法比使用相同数量样本的基线方法更为有效，并可达到使用更小数量标注样本的方法相当的性能。