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Probabilistic Decoupling of Labels in Classification (2006.09046v1)
Published 16 Jun 2020 in cs.LG and stat.ML
Abstract: In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning. We train a classifier on the given labels to predict the label-distribution. We then infer the underlying class-distributions by variationally optimizing a model of label-class transitions.
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