  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.
