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Self Semi Supervised Neural Architecture Search for Semantic Segmentation

arXiv 2201.12646 ↗First posted 29 Jan 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 49 of its 62 references that are in the map. First posted on arXiv 29 Jan 2022.

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49 of 62 references.

References · 49

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37 more
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