Dataset bias is a critical challenge in machine learning since it often leads
to a negative impact on a model due to the unintended decision rules captured
by spurious correlations. Although existing works often handle this issue based
on human supervision, the availability of the proper annotations is impractical
and even unrealistic. To better tackle the limitation, we propose a simple but
effective unsupervised debiasing technique. Specifically, we first identify
pseudo-attributes based on the results from clustering performed in the feature
embedding space even without an explicit bias attribute supervision. Then, we
employ a novel cluster-wise reweighting scheme to learn debiased
representation; the proposed method prevents minority groups from being
discounted for minimizing the overall loss, which is desirable for worst-case
generalization. The extensive experiments demonstrate the outstanding
performance of our approach on multiple standard benchmarks, even achieving the
competitive accuracy to the supervised counterpart.

该论文提出了一种简单且有效的无监督去偏差技术，该方法利用聚类算法在特征嵌入空间识别伪属性，然后采用一种新颖的聚类加权重新调整方案来学习去偏置表示，以防止少数群体被忽视并达到最坏情况下的概括，实验证明其在多个标准基准测试数据集上具有出色的表现，甚至达到了有监督对照组的竞争精度。