ACE learns an ensemble of classifiers that agree on labeled data but confidently and selectively disagree on target-distribution data, recovering diverse human-interpretable concepts under complete spurious correlation.
Learning debiased representation via disentangled feature augmentation, 2021
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ACE and Diverse Generalization via Selective Disagreement
ACE learns an ensemble of classifiers that agree on labeled data but confidently and selectively disagree on target-distribution data, recovering diverse human-interpretable concepts under complete spurious correlation.