کاربرد بسط تیلور در کاهش حجم شبکه‌های عصبی پیچشی برای طبقه‌بندی نقاشی‌های سبک امپرسیونیسم و مینیاتور

[1] L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and R. Qu, “A survey of deep learning-based object detection,” IEEE Access, vol.7, pp.128837–128868, 2019.
[2] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic seg-mentation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.580–587, 2014.
[3] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Transactions on Pattern Analysis and Machine Intel-ligence, vol.42, no.2, pp.386–397, 2020.
[4] J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” CVPR, 2017.
[5] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Con-ference on Learning Representations, 2015.
[6] 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 (CVPR), June 2016.
[7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 (F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, eds. ), pp.1097–
1105, Curran Associates, Inc., 2012.
[8] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” CoRR, vol.abs/1207.0580, 2012.
[9] L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, “Regularization of neural networks using dropconnect,” in Proceedings of the 30th International Conference on Machine Learning (S. Dasgupta and D. McAllester, eds. ), vol.28 of Proceedings of Machine Learning Research, (Atlanta, Georgia, USA), pp.1058–1066, PMLR, 17–19 Jun 2013.
[10] H. Wu and X. Gu, “Towards dropout training for convolutional neural networks,” Neural Networks, vol.71, pp.1 – 10, 2015.
[11] B. Liu, M. Wang, H. Foroosh, M. F. Tappen, and M. Pensky, “Sparse convolutional neural networks.,” in CVPR, pp.806–814, IEEE Computer Society, 2015.
[12] X. Chen, “Escort: Efficient sparse convolutional neural networks on gpus,” CoRR, vol.abs/1802.10280, 2018.
[13] W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in Advances in Neural Information Processing Systems 29 (D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, eds. ), pp.2074–2082, Curran Associates, Inc., 2016.
[14] K. Mitsuno, J. Miyao, and T. Kurita, “Hierarchical group sparse regularization for deep convolutional neural networks,” 2020.
[15] P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, “Pruning convolutional neural networks for resource efficient infer-ence,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Confer-ence Track Proceedings, OpenReview.net, 2017.
[16] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Neurocomputing: Foundations of research,” chap. Learning Representa-tions by Back-propagating Errors, pp.696–699, Cambridge, MA, USA: MIT Press, 1988.