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

Jongjin Park, Sukmin Yun, Jongheon Jeong, Jinwoo Shin

Keywords: semi-supervised learning, realistic semi-supervised learning, class-distribution mismatch, unsupervised learning

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. In this paper, we introduce OpenCoS, a method for handling this realistic semi-supervised learning scenario based on a recent framework of contrastive learning. One of our key findings is that out-of-class samples in the unlabeled dataset can be identified effectively via (unsupervised) contrastive learning. OpenCoS utilizes this information to overcome the failure modes in the existing state-of-the-art semi-supervised methods, e.g., ReMixMatch or FixMatch. In particular, we propose to assign soft-labels for out-of-class samples using the representation learned from contrastive learning. Our extensive experimental results show the effectiveness of OpenCoS, fixing the state-of-the-art semi-supervised methods to be suitable for diverse scenarios involving open-set unlabeled data.

One-sentence Summary: We utilize unsupervised representations to handle realistic semi-supervised learning, where the class distributions of labeled and unlabeled datasets do not match.

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