IMPROVING GROUP ROBUSTNESS UNDER NOISY LABELS USING PREDICTIVE UNCERTAINTY

Abstract

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 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 highuncertainty 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 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.

1. INTRODUCTION

The standard Empirical Risk Minimization (ERM) has shown a high error on specific groups of data although it achieves the low test error on the in-distribution datasets. One of the reasons accounting for such degradation is the presence of spurious-cues. The spurious cue refers to the feature which is highly correlated with labels on certain training groups-thus, easy to learn-but not correlated with other groups in the test set (Nagarajan et al., 2020; Wiles et al., 2022) . This spurious-cue is problematic especially occurs when the model cannot classify the minority samples although the model can correctly classify the majority of the training samples using the spurious cue. In practice, deep neural networks tend to fit easy-to-learn simple statistical correlations like the spurious-cues (Geirhos et al., 2020) . This problem arises in the real-world scenarios due to various factors such as an observation bias and environmental factors (Beery et al., 2018; Wiles et al., 2022) . For instance, an object detection model can predict an identical object differently simply because of the differences in the background (Ribeiro et al., 2016; Dixon et al., 2018; Xiao et al., 2020) . In nutshell, there is a low accuracy problem caused by the spurious-cues being present in a certain group of data. In that sense, importance weighting (IW) is one of the classical techniques to resolve this problem. Recently, several deep learning methods related to IW (Sagawa et al., 2019; 2020; Liu et al., 2021; Nam et al., 2020) have shown a remarkable empirical success. The main idea of those IW-related methods is to train a model with using data oversampled with hard (high-loss) samples. The assumption behind such approaches is that the high-loss samples are free from the spurious cues because these shortcut features generally reside mostly in the low-loss samples Geirhos et al. (2020) . For instance, Just-Train-Twice (JTT) trains a model using an oversampled training set containing the error set generated by the identification model. On the other hand, noisy labels are another factor of performance degradation in the real-world scenario. Noisy labels commonly occur in massive-scale human annotation data, biology and chem-

