Abstract
We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For un-labeled data, Pseudo-Labels, just picking up the class which has the maximum predicted probability, are used as if they were true la-bels. This is in effect equivalent to Entropy Regularization. It favors a low-density sepa-ration between classes, a commonly assumed prior for semi-supervised learning. With De-noising Auto-Encoder and Dropout, this sim-ple method outperforms conventional meth-ods for semi-supervised learning with very small labeled data on the MNIST handwrit-ten digit dataset. 1.
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