A. Vanyan and H. Khachatrian
|Semi-supervised learning is a branch of machine learning focused on improving the performance of models when the labeled data is scarce, but there is access to large number of unlabeled examples. Recently, there has been a remarkable process in designing algorithms which are able to get reasonable image classification accuracy having access to labels for only 0.5\% of the samples on relatively small datasets like CIFAR-10 and SVHN. The downside of these algorithms is that they require expensive tuning of hyperparameters for each dataset, and the hyperparameters tuned for one dataset do not generalize to others. In this work, we survey most of the recently proposed semi-supervised algorithms designed to work in the scope of deep learning. We highlight novelties and problems related to the robustness.