Empirical risk minimization (ERM) is sensitive to spurious correlations in
the training data, which poses a significant risk when deploying systems
trained under this paradigm in high-stake applications. While the existing
literature focuses on maximizing group-balanced or worst-group accuracy,
estimating these accuracies is hindered by costly bias annotations. This study
contends that current bias-unsupervised approaches to group robustness continue
to rely on group information to achieve optimal performance. Firstly, these
methods implicitly assume that all group combinations are represented during
training. To illustrate this, we introduce a systematic generalization task on
the MPI3D dataset and discover that current algorithms fail to improve the ERM
baseline when combinations of observed attribute values are missing. Secondly,
bias labels are still crucial for effective model selection, restricting the
practicality of these methods in real-world scenarios. To address these
limitations, we propose a revised methodology for training and validating
debiased models in an entirely bias-unsupervised manner. We achieve this by
employing pretrained self-supervised models to reliably extract bias
information, which enables the integration of a logit adjustment training loss
with our validation criterion. Our empirical analysis on synthetic and
real-world tasks provides evidence that our approach overcomes the identified
challenges and consistently enhances robust accuracy, attaining performance
which is competitive with or outperforms that of state-of-the-art methods,
which, conversely, rely on bias labels for validation.

这项研究提出了一个修订的方法来在完全无偏见的情况下进行训练和验证无偏见模型，通过采用预训练的自监督模型可靠地提取偏见信息，并与我们的验证准则结合使用逻辑调整训练损失函数。我们的实证分析证明了我们的方法克服了所发现的挑战，始终提高了鲁棒准确性，达到与或优于依赖偏见标签进行验证的最先进方法的性能。

无任何群组信息的群组鲁棒分类

Group Robust Classification Without Any Group Information

Statistical natural language inference (NLI) models are susceptible to
learning dataset bias: superficial cues that happen to associate with the label
on a particular dataset, but are not useful in general, e.g., negation words
indicate contradiction. As exposed by several recent challenge datasets, these
models perform poorly when such association is absent, e.g., predicting that "I
love dogs" contradicts "I don't love cats". Our goal is to design learning
algorithms that guard against known dataset bias. We formalize the concept of
dataset bias under the framework of distribution shift and present a simple
debiasing algorithm based on residual fitting, which we call DRiFt. We first
learn a biased model that only uses features that are known to relate to
dataset bias. Then, we train a debiased model that fits to the residual of the
biased model, focusing on examples that cannot be predicted well by biased
features only. We use DRiFt to train three high-performing NLI models on two
benchmark datasets, SNLI and MNLI. Our debiased models achieve significant
gains over baseline models on two challenge test sets, while maintaining
reasonable performance on the original test sets.

提出了 DRiFt 算法，该算法可以训练出较不受数据集偏差影响的 NLI 模型，该模型在挑战测试集上表现显著优于基准模型，并在原始测试集上保持良好的表现。