Fetching the paper…
Reading the bibliography…
Deep neural networks have exhibited promising performance in image super-resolution (SR).
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3-4, pp. 229–256, 1992
1992
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, “Learning low-level vision,” International Journal of Computer Vision , vol. 40, no. 1, pp. 25–47, 2000
2000
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 , vol. 2. IEEE, 2001, pp. 416–423
2001
Earlier work this paper cites.
R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in International conference on curves and surfaces . Springer, 2010, pp. 711–730
2010
Earlier work this paper cites.
M. D. Zeiler, G. W. Taylor, R. Fergus et al. , “Adaptive deconvolutional networks for mid and high level feature learning.” in Proceedings of the IEEE international conference on computer vision , vol. 1, no. 2, 2011, p. 6
2011
Earlier work this paper cites.
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” 2012
2012
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
Earlier work this paper cites.
J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 5197–5206
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1646–1654
2016
Earlier work this paper cites.
——, “Deeply-recursive convolutional network for image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1637–1645
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep laplacian pyramid networks for fast and accurate super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 624–632
2017
Cited alongside, same era.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
Y. Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3147–3155
2017
Cited alongside, same era.
L. Huang, J. Zhang, Y. Zuo, and Q. Wu, “Pyramid-structured depth map super-resolution based on deep dense-residual network,” IEEE Signal Processing Letters , vol. 26, no. 12, pp. 1723–1727, 2019
2019
Later among the works it cites.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” in International Conference on Machine Learning , 2019
2019
Later among the works it cites.
Y. Guo, Y. Zheng, M. Tan, Q. Chen, J. Chen, P. Zhao, and J. Huang, “Nat: Neural architecture transformer for accurate and compact architectures,” in Advances in Neural Information Processing Systems , 2019, pp. 735–747
2019
Later among the works it cites.
H. Cai, L. Zhu, and S. Han, “Proxylessnas: Direct neural architecture search on target task and hardware,” International Conference on Learning Representations , 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J.-S. Choi and M. Kim, “A deep convolutional neural network with selection units for super-resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 154–160
2017
Cited alongside, same era.
R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, and L. Zhang, “Ntire 2017 challenge on single image super-resolution: Methods and results,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 114–125
2017
Cited alongside, same era.
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 (ECCV) , 2018, pp. 286–301
2018
Cited alongside, same era.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8697–8710
2018
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in International Conference on Learning Representations, May 7-9, 2015, San Diego, CA . ICLR, 2018
2018
Cited alongside, same era.
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu, “Discrimination-aware channel pruning for deep neural networks,” in Advances in Neural Information Processing Systems , 2018, pp. 875–886
2018
Cited alongside, same era.
M. Haris, G. Shakhnarovich, and N. Ukita, “Deep back-projection networks for super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1664–1673
2018
Cited alongside, same era.
N. Ahn, B. Kang, and K.-A. Sohn, “Fast, accurate, and lightweight super-resolution with cascading residual network,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 252–268
2018
Cited alongside, same era.
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2820–2828
2019
Later among the works it cites.
X. Chen, L. Xie, J. Wu, and Q. Tian, “Progressive differentiable architecture search: Bridging the depth gap between search and evaluation,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1294–1303
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Guo, J. Chen, J. Wang, Q. Chen, J. Cao, Z. Deng, Y. Xu, and M. Tan, “Closed-loop matters: Dual regression networks for single image super-resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020
2020
Closest in time.
Y. Guo, Y. Chen, Y. Zheng, P. Zhao, J. Chen, J. Huang, and M. Tan, “Breaking the curse of space explosion: Towards efficient nas with curriculum search,” in Proceedings of the 37th International Conference on Machine Learning , 2020
2020
Closest in time.
H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han, “Once for all: Train one network and specialize it for efficient deployment,” in International Conference on Learning Representations , 2020
2020
Closest in time.
2020
Closest in time.
Y. Xu, L. Xie, X. Zhang, X. Chen, G.-J. Qi, Q. Tian, and H. Xiong, “PC-DARTS: Partial channel connections for memory-efficient architecture search,” in International Conference on Learning Representations , 2020
2020
Closest in time.