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Deep Neural Networks (DNNs) provide state-of-the-art solutions in severaldifficult machine perceptual tasks. However, their performance relies on theavailability of a large set of labeled training data, which limits the breadthof their applicability. Hence, there is a need for new {\em semi-supervisedlearning} methods for DNNs that can leverage both (a small amount of) labeledand unlabeled training data. In this paper, we develop a general loss functionenabling DNNs of any topology to be trained in a semi-supervised manner withoutextra hyper-parameters. As opposed to current semi-supervised techniques basedon topology-specific or unstable approaches, ours is both robust and general.We demonstrate that our approach reaches state-of-the-art performance on theSVHN ($9.82\%$ test error, with $500$ labels and wide Resnet) and CIFAR10(16.38% test error, with 8000 labels and sigmoid convolutional neural network)data sets.

Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion