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Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain.
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Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2014
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Are GANs created equal? A large-scale study
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Regularizing neural networks by penalizing confident output distributions
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
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On the state of the art of evaluation in neural language models
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