  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.
