论文标题
提高小组准确性差异的异常感知培训
Outlier-Aware Training for Improving Group Accuracy Disparities
论文作者
论文摘要
解决虚假相关性的方法，例如仅两次训练（JTT，ARXIV：2107.09044V2），涉及重新批准训练集的子集，以最大程度地提高最差的组精度。但是，重新加权的一组示例可能可能包含阻碍模型学习的无可估计示例。我们建议通过检测到培训设置的离群值并在重新加权之前将其删除来缓解这种情况。我们的实验表明，与JTT相比，我们的方法达到了竞争性或更高的精度，并且可以检测和删除JTT中的子集中的注释错误。
Methods addressing spurious correlations such as Just Train Twice (JTT, arXiv:2107.09044v2) involve reweighting a subset of the training set to maximize the worst-group accuracy. However, the reweighted set of examples may potentially contain unlearnable examples that hamper the model's learning. We propose mitigating this by detecting outliers to the training set and removing them before reweighting. Our experiments show that our method achieves competitive or better accuracy compared with JTT and can detect and remove annotation errors in the subset being reweighted in JTT.
