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A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data.
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Generalized zero-shot learning with deep calibration network
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Realistic evaluation of deep semi-supervised learning algorithms
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Maria Brbic, Marinka Zitnik, Sheng Wang, Angela O Pisco, Russ B Altman, Spyros Darmanis, and Jure Leskovec · 2020
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Recent advances in open set recognition: A survey
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Safe deep semi-supervised learning for unseen-class unlabeled data
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Negative margin matters: Understanding margin in few-shot classification
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Universal domain adaptation through self supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko · 2020
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FixMatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Multi-task curriculum framework for open-set semi-supervised learning
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa · 2020
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OpenMix: Reviving known knowledge for discovering novel visual categories in an open world
Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe · 2020
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