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State-of-the-art algorithms for many semantic visual tasks are based on the use of convolutional neural networks.
Removing camera shake from a single photograph
R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Understanding and evaluating blind deconvolution algorithms
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman · 2009
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The PASCAL Visual Object Classes (VOC) Challenge
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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A machine learning approach for non-blind image deconvolution
C. J. Schuler, H. C. Burger, S. Harmeling, and B. Scholkopf · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Blind deblurring using internal patch recurrence
T. Michaeli and M. Irani · 2014
Cited alongside, same era.
Learning to deblur
C. J. Schuler, M. Hirsch, S. Harmeling, and B. Schölkopf · 2014
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Cited alongside, same era.
Deep convolutional neural network for image deconvolution
L. Xu, J. S. Ren, C. Liu, and J. Jia · 2014
Cited alongside, same era.
Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Trunk-branch ensemble convolutional neural networks for video-based face recognition
C. Ding and D. Tao · 2016
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Understanding how image quality affects deep neural networks
S. Dodge and L. Karam · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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How image degradations affect deep CNN-based face recognition?
S. Karahan, M. K. Yildirim, K. Kirtac, F. S. Rende, G. Butun, and H. K. Ekenel · 2016
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Faceless person recognition: Privacy implications in social media
S. J. Oh, R. Benenson, M. Fritz, and B. Schiele · 2016
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Fine-to-coarse knowledge transfer for low-res image classification
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
A neural approach to blind motion deblurring
A. Chakrabarti · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Cited alongside, same era.
X. Peng, J. Hoffman, S. X. Yu, and K. Saenko · 2016
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Psyphy: A psychophysics driven evaluation framework for visual recognition
B. RichardWebster, S. E. Anthony, and W. J. Scheirer · 2016
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Atoms of recognition in human and computer vision
S. Ullman, L. Assif, E. Fetaya, and D. Harari · 2016
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On classification of distorted images with deep convolutional neural networks
Y. Zhou, S. Song, and N.-M. Cheung · 2017
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