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Image super-resolution (SR) is a representative low-level vision problem.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
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Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets
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Nonlinear dimensionality reduction by locally linear embedding
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A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum, V. De Silva, and J. C. Langford · 2000
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Stochastic neighbor embedding
G. Hinton and S. T. Roweis · 2002
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A statistical evaluation of recent full reference image quality assessment algorithms
H. R. Sheikh, M. F. Sabir, and A. C. Bovik · 2006
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Image denoising by sparse 3-d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
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Learning realistic human actions from movies
I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld · 2008
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Visualizing data using t-sne
L. Van der Maaten and G. Hinton · 2008
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Image super-resolution as sparse representation of raw image patches
J. Yang, J. Wright, T. Huang, and Y. Ma · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Deconvolutional networks
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
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Convolutional neural networks for no-reference image quality assessment
L. Kang, P. Ye, Y. Li, and D. Doermann · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Visualizing and understanding recurrent networks
A. Karpathy, J. Johnson, and L. Fei-Fei · 2015
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Visualizing and understanding neural models in nlp
J. Li, X. Chen, E. Hovy, and D. Jurafsky · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Human-level control through deep reinforcement learning
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Dehazenet: An end-to-end system for single image haze removal
B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao · 2016
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Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Diverse image-to-image translation via disentangled representations
H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang · 2018
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Disentangled person image generation
L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele, and M. Fritz · 2018
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Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K.-R. Müller · 2018
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Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Ntire 2018 challenge on single image super-resolution: Methods and results
R. Timofte, S. Gu, J. Wu, L. Van Gool, L. Zhang, M.-H. Yang, M. Haris, et al · 2018
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Esrgan: Enhanced super-resolution generative adversarial networks
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Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Visualizing deep convolutional neural networks using natural pre-images
A. Mahendran and A. Vedaldi · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Cited alongside, same era.
A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
Cited alongside, same era.
Graying the black box: Understanding dqns
T. Zahavy, N. Ben-Zrihem, and S. Mannor · 2016
Cited alongside, same era.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. C. Loy · 2018
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Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks
Y. Yuan, S. Liu, J. Zhang, Y. Zhang, C. Dong, and L. Lin · 2018
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Densely connected pyramid dehazing network
H. Zhang and V. M. Patel · 2018
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Image reconstruction by domain-transform manifold learning
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen · 2018
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Blind super-resolution kernel estimation using an internal-gan
S. Bell-Kligler, A. Shocher, and M. Irani · 2019
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Second-order attention network for single image super-resolution
T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang · 2019
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Blind super-resolution with iterative kernel correction
J. Gu, H. Lu, W. Zuo, and C. Dong · 2019
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
S. Lee, D. Kim, N. Kim, and S.-G. Jeong · 2019
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Srobb: Targeted perceptual loss for single image super-resolution
M. S. Rad, B. Bozorgtabar, U.-V. Marti, M. Basler, H. K. Ekenel, and J.-P. Thiran · 2019
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Ranksrgan: Generative adversarial networks with ranker for image super-resolution
W. Zhang, Y. Liu, C. Dong, and Y. Qiao · 2019
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Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing
Y. Dong, Y. Liu, H. Zhang, S. Chen, and Y. Qiao · 2020
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Conditional sequential modulation for efficient global image retouching
J. He, Y. Liu, Y. Qiao, and C. Dong · 2020
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Understanding generalization through visualizations
W. R. Huang, Z. Emam, M. Goldblum, L. Fowl, J. K. Terry, F. Huang, and T. Goldstein · 2020
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Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
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Deep multi-label learning for image distortion identification
D. Liang, X. Gao, W. Lu, and L. He · 2020
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Unfolding the alternating optimization for blind super resolution
Z. Luo, Y. Huang, S. Li, L. Wang, and T. Tan · 2020
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Face identity disentanglement via latent space mapping
Y. Nitzan, A. Bermano, Y. Li, and D. Cohen-Or · 2020
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Self2self with dropout: Learning self-supervised denoising from single image
Y. Quan, M. Chen, T. Pang, and H. Ji · 2020
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Closed-form factorization of latent semantics in gans
Y. Shen and B. Zhou · 2020
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A survey on neural network interpretability
Y. Zhang, P. Tiňo, A. Leonardis, and K. Tang · 2020
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Interpreting super-resolution networks with local attribution maps
J. Gu and C. Dong · 2021
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Blind image super-resolution: A survey and beyond
A. Liu, Y. Liu, J. Gu, Y. Qiao, and C. Dong · 2021
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Designing a practical degradation model for deep blind image super-resolution
K. Zhang, J. Liang, L. Van Gool, and R. Timofte · 2021
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Evaluating the generalization ability of super-resolution networks
Y. Liu, H. Zhao, J. Gu, Y. Qiao, and C. Dong · 2022
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