RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss
Trung Q. Tran,
Mingu Kang,
Daeyoung Kim
Abstract:Semi-supervised learning (SSL) has played an important role in leveraging unlabeled data when labeled data is limited. One of the most successful SSL approaches is based on consistency regularization, which encourages the model to produce unchanged with perturbed input. However, there has been less attention spent on inputs that have the same label. Motivated by the observation that the inputs having the same label should have the similar model outputs, we propose a novel method, RankingMatch, that considers n… Show more
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