Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

C. Wang, Z. Li, and J. Shi, “Lightweight image super-resolution with adaptive weighted learning network,” arXiv preprint arXiv:1904.02358 , 04 2019. [Online]. Available: https://arxiv.org/abs/1904.02358

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C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in European Conference on Computer Vision . Springer, 2016, pp. 391–407

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J. Kim, J. Kwon Lee, and K. Mu Lee, “Deeply-recursive convolutional network for image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1637–1645

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Z. Lu, I. Whalen, V. Boddeti, Y. Dhebar, K. Deb, E. Goodman, and W. Banzhaf, “Nsga-net: A multi-objective genetic algorithm for neural architecture search,” arXiv preprint arXiv:1810.03522 , 2018

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Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

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Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proceedings of the European Conference on Computer Vision, Munich, Germany , 2018, pp. 8–14

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