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Image classification is an essential task in computer vision, which aims to categorise a set of images into different groups based on some visual criteria.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature , vol. 323, no. 6088, pp. 533–536, 1986
1986
Earlier work this paper cites.
J. Triesch and C. Von Der Malsburg, “Robust classification of hand postures against complex backgrounds,” in Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on . IEEE, 1996, pp. 170–175
1996
Earlier work this paper cites.
T. M. Mitchell et al. , “Machine learning. wcb,” 1997
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
M. Zhang and W. Smart, “Genetic programming with gradient descent search for multiclass object classification,” in EuroGP . Springer, 2004, pp. 399–408
2004
Earlier work this paper cites.
W. Smart and M. Zhang, “Applying online gradient descent search to genetic programming for object recognition,” in Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation-Volume 32 . Australian Computer Society, Inc., 2004, pp. 133–138
2004
Earlier work this paper cites.
V. Podlozhnyuk, “Image convolution with cuda,” NVIDIA Corporation white paper, June , vol. 2097, no. 3, 2007
2007
Earlier work this paper cites.
F. Cheng, J. Yu, and H. Xiong, “Facial expression recognition in jaffe dataset based on gaussian process classification,” IEEE Transactions on Neural Networks , vol. 21, no. 10, pp. 1685–1690, 2010
2010
Earlier work this paper cites.
G. E. Hinton, A. Krizhevsky, and S. D. Wang, “Transforming auto-encoders,” in International Conference on Artificial Neural Networks . Springer, 2011, pp. 44–51
2011
Earlier work this paper cites.
R. Gopalan, R. Li, and R. Chellappa, “Domain adaptation for object recognition: An unsupervised approach,” in Computer Vision (ICCV), 2011 IEEE International Conference on . IEEE, 2011, pp. 999–1006
2011
Cited alongside, same era.
J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research , vol. 12, no. Jul, pp. 2121–2159, 2011
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Cited alongside, same era.
M. D. Zeiler, “Adadelta: an adaptive learning rate method,” arXiv preprint arXiv:1212.5701 , 2012
2012
Cited alongside, same era.
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
Later among the works it cites.
Z. Emigdio, L. Trujillo, O. Schütze, P. Legrand et al. , “A local search approach to genetic programming for binary classification,” in Proceedings of the 2015 on Genetic and Evolutionary Computation Conference-GECCO’15 , 2015
2015
Later among the works it cites.
D. Batra, “Deep learning for perception: Backprop in cnns (lecture 5 notes),” Fall 2015
2015
Later among the works it 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
Later among the works it cites.
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2013
Cited alongside, same era.
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
Cited alongside, same era.
R. M. A. Azad and C. Ryan, “A simple approach to lifetime learning in genetic programming-based symbolic regression,” Evolutionary computation , vol. 22, no. 2, pp. 287–317, 2014
2014
Cited alongside, same era.
T. Desell, “Large scale evolution of convolutional neural networks using volunteer computing,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion . ACM, 2017, pp. 127–128
2017
Later among the works it cites.
M. Suganuma, S. Shirakawa, and T. Nagao, “A genetic programming approach to designing convolutional neural network architectures,” in Proceedings of the Genetic and Evolutionary Computation Conference . ACM, 2017, pp. 497–504
2017
Later among the works it cites.
T. Leonardo, Z. Emigdio, P. S. J. Smith, P. Legrand, S. Silva, M. Castelli, L. Vanneschi, O. Schütze, L. Munoz et al. , “Local search is underused in genetic programming,” 2017
2017
Later among the works it cites.
B. Evans, H. Al-Sahaf, B. Xue, and M. Zhang, Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification. , 2018
2018
Later among the works it cites.