  Recently, a number of new Semi-Supervised Learning methods have emerged. As
the accuracy for ImageNet and similar datasets increased over time, the
performance on tasks beyond the classification of natural images is yet to be
explored. Most Semi-Supervised Learning methods rely on a carefully manually
designed data augmentation pipeline that is not transferable for learning on
images of other domains. In this work, we propose a Semi-Supervised Learning
method that automatically selects the most effective data augmentation policy
for a particular dataset. We build upon the Fixmatch method and extend it with
meta-learning of augmentations. The augmentation is learned in additional
training before the classification training and makes use of bi-level
optimization, to optimize the augmentation policy and maximize accuracy. We
evaluate our approach on two domain-specific datasets, containing satellite
images and hand-drawn sketches, and obtain state-of-the-art results. We further
investigate in an ablation the different parameters relevant for learning
augmentation policies and show how policy learning can be used to adapt
augmentations to datasets beyond ImageNet.
