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• W3123671289 abstract "Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one aims to apply SSL when unlabeled data may contain out-of-class samples - those that cannot have one-hot encoded labels from a closed-set of classes in label data, i.e., the unlabeled data is an open-set. Specifically, we introduce OpenCoS, a simple framework for handling this realistic semi-supervised learning scenario based upon a recent framework of self-supervised visual representation learning. We first observe that the out-of-class samples in the open-set unlabeled dataset can be identified effectively via self-supervised contrastive learning. Then, OpenCoS utilizes this information to overcome the failure modes in the existing state-of-the-art semi-supervised methods, by utilizing one-hot pseudo-labels and soft-labels for the identified in- and out-of-class unlabeled data, respectively. Our extensive experimental results show the effectiveness of OpenCoS under the presence of out-of-class samples, fixing up the state-of-the-art semi-supervised methods to be suitable for diverse scenarios involving open-set unlabeled data." @default.
• W3123671289 created "2021-02-01" @default.
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• W3123671289 date "2021-06-29" @default.
• W3123671289 modified "2023-10-16" @default.
• W3123671289 title "OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data" @default.
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• W3123671289 doi "https://doi.org/10.48550/arxiv.2107.08943" @default.
• W3123671289 hasPublicationYear "2021" @default.
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