  We introduce Interpolation Consistency Training (ICT), a simple and
computation efficient algorithm for training Deep Neural Networks in the
semi-supervised learning paradigm. ICT encourages the prediction at an
interpolation of unlabeled points to be consistent with the interpolation of
the predictions at those points. In classification problems, ICT moves the
decision boundary to low-density regions of the data distribution. Our
experiments show that ICT achieves state-of-the-art performance when applied to
standard neural network architectures on the CIFAR-10 and SVHN benchmark
datasets. Our theoretical analysis shows that ICT corresponds to a certain type
of data-adaptive regularization with unlabeled points which reduces overfitting
to labeled points under high confidence values.
