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An Empirical Study and Analysis on Open-Set Semi-Supervised Learning

By Huixiang Luo and others
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods. These methods exhibit great power in many machine learning tasks by utilizing unlabeled data for efficient training. But in a more realistic setting (termed as open-set SSL), where unlabeled dataset contains out-of-distribution... Show more
September 14, 2021
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An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
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