# Self Semi Supervised Neural Architecture Search for Semantic Segmentation Source: [Self Semi Supervised Neural Architecture Search for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:54:05.632Z Authors: Lo\\"ic Pauletto, Massih-Reza Amini, Nicolas Winckler Published: 2022-01-29T19:49:44.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()