  Recent semi-supervised learning methods have shown to achieve comparable
results to their supervised counterparts while using only a small portion of
labels in image classification tasks thanks to their regularization strategies.
In this paper, we take a more direct approach for semi-supervised learning and
propose learning to impute the labels of unlabeled samples such that a network
achieves better generalization when it is trained on these labels. We pose the
problem in a learning-to-learn formulation which can easily be incorporated to
the state-of-the-art semi-supervised techniques and boost their performance
especially when the labels are limited. We demonstrate that our method is
applicable to both classification and regression problems including image
classification and facial landmark detection tasks.
