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Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community.
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Microsoft coco: Common objects in context
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A survey on semi-supervised learning techniques
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Dropout: a simple way to prevent neural networks from overfitting
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Unsupervised data augmentation for consistency training
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Deep clustering for unsupervised learning of visual features
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Unsupervised feature learning via non-parametric instance discrimination
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A theoretical analysis of contrastive unsupervised representation learning
S. Arora, Hrishikesh Khandeparkar, M. Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
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Mean shift for self-supervised learning
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Prototypical contrastive learning of unsupervised representations
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Solving inefficiency of self-supervised representation learning
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