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Deep neural networks (DNNs) achieve excellent performance on standard classification tasks.
T. Bachmann, “Identification of spatially quantised tachistoscopic images of faces: How many pixels does it take to carry identity?” European Journal of Cognitive Psychology , vol. 3, no. 1, pp. 87–103, 1991
1991
Earlier work this paper cites.
A. Torralba, R. Fergus, and W. T. Freeman, “80 million tiny images: A large data set for nonparametric object and scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 11, pp. 1958–1970, 2008
2008
Earlier work this paper cites.
F. Fleuret, T. Li, C. Dubout, E. K. Wampler, S. Yantis, and D. Geman, “Comparing machines and humans on a visual categorization test,” Proceedings of the National Academy of Sciences , vol. 108, no. 43, pp. 17 621–17 625, 2011
2011
Earlier work this paper cites.
D. Parikh, “Recognizing jumbled images: The role of local and global information in image classification,” in IEEE International Conference on Computer Vision (ICCV) , 2011, pp. 519–526
2011
Earlier work this paper cites.
A. Borji and L. Itti, “Human vs. computer in scene and object recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014, pp. 113–120
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
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, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
Cited alongside, same era.
Y. Chen, R. McBain, and D. Norton, “Specific vulnerability of face perception to noise: A similar effect in schizophrenia patients and healthy individuals,” Psychiatry research , vol. 225, no. 3, pp. 619–624, 2015
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1–9, 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
2016
Later among the works it cites.
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016. [Online]. Available: http://goodfeli.github.io/dlbook/
2016
Later among the works it cites.
2017
Closest in time.
S. Dodge and L. Karam, “Quality resilient neural networks,” arXiv preprint arXiv:1703.08119 , 2017
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Cited alongside, same era.
2016
Cited alongside, same era.
S. Dodge and L. Karam, “Understanding how image quality affects deep neural networks,” International Conference on Quality of Multimedia Experience (QoMEX) , 2016
2016
Cited alongside, same era.
S. Stabinger, A. Rodríguez-Sánchez, and J. Piater, “25 years of cnns: Can we compare to human abstraction capabilities?” in International Conference on Artificial Neural Networks . Springer, 2016, pp. 380–387
2016
Cited alongside, same era.
2017
Closest in time.