Self Semi Supervised Neural Architecture Search for Semantic Segmentation

• URL: http://arxiv.org/abs/2201.12646v2
• Date: Tue, 1 Feb 2022 04:16:47 GMT
• Title: Self Semi Supervised Neural Architecture Search for Semantic Segmentation
• Authors: Lo\"ic Pauletto and Massih-Reza Amini and Nicolas Winckler
• Abstract summary: 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 model for this task. 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.
• Score: 6.488575826304023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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