本文简单介绍了NAS的发展现况和在语义分割中的应用，并且详细解读了两篇流行的work：DARTS和Auto-DeepLab。从整体实验结果来看，还不能看出NAS的方法比传统的模型有压倒性优势，尤其在准确度上。但是NAS给深度学习注入了新鲜的血液，为研究者提供了一种新的思路，并且还有很大的提升空间和待开发领域。也许人工设计网络结构将会被自动网络搜索取代。
翻译或有误差，请参考原文https://medium.com/@majingting2014/neural-architecture-search-on-semantic-segmentation-1801ee48d6c4
对这块比较关注的同学可以移步继续阅读《自动网络搜索（NAS）在语义分割上的应用（二）》
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[3] Zoph, Barret, et al. "Learning transferable architectures for scalable image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2018.
[4] Zhang, Yiheng, et al. "Customizable Architecture Search for Semantic Segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
[5] Chen, Liang-Chieh, et al. "Searching for efficient multi-scale architectures for dense image prediction." Advances in Neural Information Processing Systems (NIPS). 2018.
[6] Liu, Chenxi, et al. "Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
[7] Nekrasov, Vladimir, et al. "Fast neural architecture search of compact semantic segmentation models via auxiliary cells." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
[8] Lin, Peiwen, et al. "Graph-guided Architecture Search for Real-time Semantic Segmentation." arXiv preprint arXiv:1909.06793 (2019).
[9] Liu, Hanxiao, Karen Simonyan, and Yiming Yang. "Darts: Differentiable architecture search." The International Conference on Learning Representations (ICLR) (2019).
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