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Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

Semi-supervised learning (SSL) based on deep neural networks have recently proven successful on standard benchmark tasks. Baselines which do not use unlabeled data is often underreported, SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and performance can degrade substantially when the unlabeled dataset contains out-of- distribution examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available.

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