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OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data

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Abstract

Modern semi-supervised learning methods conventionally assume both labeled and unlabeled data have the same class distribution. However, unlabeled data may include out-of-class samples in practice; those that cannot have one-hot encoded labels from a closed-set of classes in label data, i.e., unlabeled data is an open-set... (read more)

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https://openreview.net/pdf?id=lJgbDxGhJ4r

0001-01-01 -