E. Hamed-habibi-aghdam and . Heravi, , 2017.

L. E. Atlas, T. Homma, and R. J. Marks-ii, An artificial neural network for spatiotemporal bipolar patterns: Application to phoneme classification, Neural Information Processing Systems, pp.31-40, 1987.

P. L. Callet, C. Viard-gaudin, and D. Barba, A convolutional neural network approach for objective video quality assessment, IEEE Transactions on Neural Networks, vol.17, issue.5, pp.1316-1343, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00287426

D. S. Clouse, C. L. Giles, B. G. Horne, and G. W. Cottrell, Time-delay neural networks: representation and induction of finite-state machines, IEEE Transactions on Neural Networks, vol.8, issue.5, pp.1065-70, 1997.

C. Dan, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, Computer Vision and Pattern Recognition, pp.3642-3649, 2012.

P. O. Glauner, Deep convolutional neural networks for smile recognition, IEEE/ACM Transactions on Audio Speech and Language Processing, vol.22, issue.10, pp.1533-1545, 2015.

S. Haykin and . Kosko, GradientBased Learning Applied to Document Recognition, 2009.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

H. Hu, L. Pang, D. Tian, and Z. Shi, Perception granular computing in visual haze-free task, Expert Systems with Applications, vol.41, issue.6, pp.2729-2741, 2014.

G. Huang, Q. Zhu, and C. Siew, Extreme learning machine: theory and applications, Neurocomputing, vol.70, issue.1, pp.489-501, 2006.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Communications of the Acm, vol.60, issue.2, p.2012, 2012.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 2001.

. Vernon-b-mountcastle, The columnar organization of the neocortex, Brain: a journal of neurology, vol.120, issue.4, pp.701-722, 1997.

Q. Sinno-jialin-pan and . Yang, A survey on transfer learning, IEEE Transactions on knowledge and data engineering, vol.22, issue.10, pp.1345-1359, 2010.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, 1988.

D. E. Rumelhart, J. L. Mcclelland, and T. Group, Parallel distributed processing: Foundations v. 1: Explorations in the microstructure of cognition, Language, vol.63, issue.4, pp.45-76, 1986.

J. Schmidhuber, U. Meier, and D. Ciresan, Multi-column deep neural networks for image classification, vol.157, pp.3642-3649, 2012.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., , 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2818-2826, 2016.

S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, 2016.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, How transferable are features in deep neural networks?, Advances in neural information processing systems, pp.3320-3328, 2014.