Empirical risk minimization (ERM) of neural networks is prone to
over-reliance on spurious correlations and poor generalization on minority
groups. The recent deep feature reweighting (DFR) technique achieves
state-of-the-art group robustness via simple last-layer retraining, but it
requires held-out group and class annotations to construct a group-balanced
reweighting dataset. In this work, we examine this impractical requirement and
find that last-layer retraining can be surprisingly effective with no group
annotations (other than for model selection) and only a handful of class
annotations. We first show that last-layer retraining can greatly improve
worst-group accuracy even when the reweighting dataset has only a small
proportion of worst-group data. This implies a "free lunch" where holding out a
subset of training data to retrain the last layer can substantially outperform
ERM on the entire dataset with no additional data or annotations. To further
improve group robustness, we introduce a lightweight method called selective
last-layer finetuning (SELF), which constructs the reweighting dataset using
misclassifications or disagreements. Our empirical and theoretical results
present the first evidence that model disagreement upsamples worst-group data,
enabling SELF to nearly match DFR on four well-established benchmarks across
vision and language tasks with no group annotations and less than 3% of the
held-out class annotations. Our code is available at
this https URL

通过仅使用模型选择的族群注释和少数类别注释，最后一层的重新训练可以显著提高最差群组的准确性，而无需额外的数据或注释。此外，我们还引入了一种轻量级方法，即选择性的最后一层微调（SELF），通过使用误分类或不一致性的数据构建了重新加权数据集，实现了在视觉和语言任务的四个基准测试中无需族群注释和少于 3％的保留类别注释即可与 DFR 相媲美。

面向少量注释的群体鲁棒性的最后一层重新训练

Towards Last-layer Retraining for Group Robustness with Fewer  Annotations

Standard training via empirical risk minimization (ERM) can produce models
that achieve high accuracy on average but low accuracy on certain groups,
especially in the presence of spurious correlations between the input and
label. Prior approaches that achieve high worst-group accuracy, like group
distributionally robust optimization (group DRO) require expensive group
annotations for each training point, whereas approaches that do not use such
group annotations typically achieve unsatisfactory worst-group accuracy. In
this paper, we propose a simple two-stage approach, JTT, that first trains a
standard ERM model for several epochs, and then trains a second model that
upweights the training examples that the first model misclassified.
Intuitively, this upweights examples from groups on which standard ERM models
perform poorly, leading to improved worst-group performance. Averaged over four
image classification and natural language processing tasks with spurious
correlations, JTT closes 75% of the gap in worst-group accuracy between
standard ERM and group DRO, while only requiring group annotations on a small
validation set in order to tune hyperparameters.

本文提出了 JTT 这样一个简单的两阶段方法，利用加重权重的方式提高标准 ERM 模型对于性能低下群组的准确性，从而填补了标准 ERM 模型与 group DRO 之间最差群组准确性差距的 75％，并且只需要在小型验证集上标注群体注释以调整超参数。