  Neural Architecture Search (NAS) has been used recently to achieve improved
performance in various tasks and most prominently in image classification. Yet,
current search strategies rely on large labeled datasets, which limit their
usage in the case where only a smaller fraction of the data is annotated.
Self-supervised learning has shown great promise in training neural networks
using unlabeled data. In this work, we propose a self-supervised neural
architecture search (SSNAS) that allows finding novel network models without
the need for labeled data. We show that such a search leads to comparable
results to supervised training with a "fully labeled" NAS and that it can
improve the performance of self-supervised learning. Moreover, we demonstrate
the advantage of the proposed approach when the number of labels in the search
is relatively small.
