  This paper proposes a novel cell-based neural architecture search algorithm
(NAS), which completely alleviates the expensive costs of data labeling
inherited from supervised learning. Our algorithm capitalizes on the
effectiveness of self-supervised learning for image representations, which is
an increasingly crucial topic of computer vision. First, using only a small
amount of unlabeled train data under contrastive self-supervised learning allow
us to search on a more extensive search space, discovering better neural
architectures without surging the computational resources. Second, we entirely
relieve the cost for labeled data (by contrastive loss) in the search stage
without compromising architectures' final performance in the evaluation phase.
Finally, we tackle the inherent discrete search space of the NAS problem by
sequential model-based optimization via the tree-parzen estimator (SMBO-TPE),
enabling us to reduce the computational expense response surface significantly.
An extensive number of experiments empirically show that our search algorithm
can achieve state-of-the-art results with better efficiency in data labeling
cost, searching time, and accuracy in final validation.
