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• W3189925607 abstract "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." @default.
• W3189925607 created "2021-08-16" @default.
• W3189925607 creator A5006594639 @default.
• W3189925607 creator A5044267273 @default.
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• W3189925607 date "2021-08-06" @default.
• W3189925607 modified "2023-09-24" @default.
• W3189925607 title "Unsupervised Learning of Debiased Representations with Pseudo-Attributes" @default.
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• W3189925607 doi "https://doi.org/10.48550/arxiv.2108.02943" @default.
• W3189925607 hasPublicationYear "2021" @default.
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