  We exploit a recently derived inversion scheme for arbitrary deep neural
networks to develop a new semi-supervised learning framework that applies to a
wide range of systems and problems. The approach outperforms current
state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while
using $5$ labeled examples per class. Experiments with one-dimensional signals
highlight the generality of the method. Importantly, our approach is simple,
efficient, and requires no change in the deep network architecture.
