A class-wise positive-unlabeled risk estimator trains classifiers from ambiguous data with hard labels and beats label-noise baselines on synthetic mixed-image benchmarks.
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Learning from Ambiguous Data with Hard Labels
A class-wise positive-unlabeled risk estimator trains classifiers from ambiguous data with hard labels and beats label-noise baselines on synthetic mixed-image benchmarks.