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

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

URL: http://arxiv.org/abs/2107.08943v1
Date: Tue, 29 Jun 2021 06:10:05 GMT
Title: OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Authors: Jongjin Park, Sukmin Yun, Jongheon Jeong, Jinwoo Shin
Abstract summary: Unlabeled data may include out-of-class samples in practice. OpenCoS is a method for handling this realistic semi-supervised learning scenario.
Score: 65.19205979542305
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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. It further improves the semi-supervised performance by utilizing soft- and pseudo-labels on open-set unlabeled data, 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.

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