Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection
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Pseudo-labeling solutions for positive-unlabeled (PU) learning have the potential to result in higher performance compared to cost-sensitive learning but are vulnerable to incorrectly estimated pseudo-labels. In this paper, we provide a theoretical analysis of a risk estimator that combines risk on PU and pseudo-labeled data. Furthermore, we show analytically as well as experimentally that such an estimator results in lower excess risk compared to using PU data alone, provided that enough samples are pseudo-labeled with acceptable error rates. We then propose PUUPL, a novel training procedure for PU learning that leverages the epistemic uncertainty of an ensemble of deep neural networks to minimize errors in pseudo-label selection. We conclude with extensive experiments showing the effectiveness of our proposed algorithm over different datasets, modalities, and learning tasks. These show that PUUPL enables a reduction of up to 20 negative samples are not provided for validation, setting a new state-of-the-art for PU learning.
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