  In this paper, we propose a Neural Architecture Search strategy based on self
supervision and semi-supervised learning for the task of semantic segmentation.
Our approach builds an optimized neural network (NN) model for this task by
jointly solving a jigsaw pretext task discovered with self-supervised learning
over unlabeled training data, and, exploiting the structure of the unlabeled
data with semi-supervised learning. The search of the architecture of the NN
model is performed by dynamic routing using a gradient descent algorithm.
Experiments on the Cityscapes and PASCAL VOC 2012 datasets demonstrate that the
discovered neural network is more efficient than a state-of-the-art
hand-crafted NN model with four times less floating operations.
