It is well-known that training neural networks for image classification with empirical risk minimization (ERM) makes them vulnerable to relying on spurious attributes instead of causal ones for prediction. Previously, deep feature re-weighting (DFR) has proposed retraining the last layer of a pre-trained network on balanced data concerning spurious attributes, making it robust to spurious correlation. However, spurious attribute annotations are not always available. In order to provide group robustness without such annotations, we propose a new method, called loss-based feature re-weighting (LFR), in which we infer a grouping of the data by evaluating an ERM-pre-trained model on a small left-out split of the training data. Then, a balanced number of samples is chosen by selecting high-loss samples from misclassified data points and low-loss samples from correctly-classified ones. Finally, we retrain the last layer on the selected balanced groups to make the model robust to spurious correlation. For a complete assessment, we evaluate LFR on various versions of Waterbirds and CelebA datasets with different spurious correlations, which is a novel technique for observing the model's performance in a wide range of spuriosity rates. While LFR is extremely fast and straightforward, it outperforms the previous methods that do not assume group label availability, as well as the DFR with group annotations provided, in cases of high spurious correlation in the training data.

翻译：众所周知，通过经验风险最小化（ERM）训练用于图像分类的神经网络，会使模型容易依赖虚假属性而非因果属性进行预测。此前，深度特征重加权（DFR）提出在关于虚假属性的平衡数据上重新训练预训练网络的最后一层，使其对虚假相关性具有鲁棒性。然而，虚假属性注释并非始终可用。为了在无此类注释的情况下实现组鲁棒性，我们提出一种名为基于损失的特征重加权（LFR）的新方法：通过评估在训练数据的小型预留子集上预训练的ERM模型来推断数据分组。随后，从误分类数据点中选择高损失样本，从正确分类数据点中选择低损失样本，从而选取平衡数量的样本。最后，我们在所选平衡组上重新训练最后一层，使模型对虚假相关性具有鲁棒性。为进行全面评估，我们在具有不同虚假相关性的Waterbirds和CelebA数据集的各种版本上评估LFR——这是一种观察模型在广泛虚假性率下性能的新颖技术。尽管LFR极其快速且直接，但在训练数据中存在高虚假相关性的情况下，它优于此前不假设组标签可用性的方法，甚至超越提供组注释的DFR。