Se2NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation

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 solving a jigsaw pretext problem identified by self-supervised learning over unlabeled training data, and, leveraging the structure of the unlabeled data with semi-supervised learning. Dynamic routing with a gradient descent approach is used to find the architecture of the NN model. Experiments on the Cityscapes and PASCAL VOC 2012 datasets show that the found neural network is four times more efficient than a state-of-the-art hand designed NN model in terms of floating-point operations.