  The standard empirical risk minimization (ERM) can underperform on certain
minority groups (i.e., waterbirds in lands or landbirds in water) due to the
spurious correlation between the input and its label. Several studies have
improved the worst-group accuracy by focusing on the high-loss samples. The
hypothesis behind this is that such high-loss samples are
\textit{spurious-cue-free} (SCF) samples. However, these approaches can be
problematic since the high-loss samples may also be samples with noisy labels
in the real-world scenarios. To resolve this issue, we utilize the predictive
uncertainty of a model to improve the worst-group accuracy under noisy labels.
To motivate this, we theoretically show that the high-uncertainty samples are
the SCF samples in the binary classification problem. This theoretical result
implies that the predictive uncertainty is an adequate indicator to identify
SCF samples in a noisy label setting. Motivated from this, we propose a novel
ENtropy based Debiasing (END) framework that prevents models from learning the
spurious cues while being robust to the noisy labels. In the END framework, we
first train the \textit{identification model} to obtain the SCF samples from a
training set using its predictive uncertainty. Then, another model is trained
on the dataset augmented with an oversampled SCF set. The experimental results
show that our END framework outperforms other strong baselines on several
real-world benchmarks that consider both the noisy labels and the
spurious-cues.
