참고문헌
- K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position", Biological Cybernetics, vol. 36, no. 4, pp. 193-202, 1980. https://doi.org/10.1007/BF00344251
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haifner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
- I.-J. Kim and X. Xie. "Handwritten Hangul recognition using deep convolutional neural networks." International Journal on Document Analysis and Recognition, vol. 18, no.1, pp. 1-13, 2014. https://doi.org/10.1007/s10032-014-0229-4
- I. Goodfellow, et al. "Maxout networks", arXiv preprint arXiv: 1302.4389, 2013.
- M. Lin, Q. Chen, and S. Yan, "Network in network", Proc. ICLR, 2014.
- K. He, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition", Computer Vision - ECCV 2014. Springer International Publishing, 346-361, 2014.
- C. Szegedy, et al. "Going deeper with convolutions." arXiv preprint arXiv:1409.4842, 2014.
- D. Ciresan and J. Schmidhuber, "Multi-column deep new-al networks for omine handwritten Chinese character classification", arXiv preprint arXiv: 1309. 0261, 2013.
- A. Krizhevsky, I. Sutskever, and G. Hinton. "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems. 2012.
- K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556, 2014.
- https://en.wikipedia.org/wiki/Regularization_(mathematics)
- G. Hinton, et al. "Improving neural networks by preventing co-adaptation of feature detectors", arXiv preprint arXiv:1207.0580, 2012.
- Y. Bengio, et al. "Curriculum learning." Proc. 26th annual international conference on machine learning. ACM, 2009.
- S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", arXiv preprint arXiv: 1502.03167, 2015.
- H. Lee, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations." Proc. 26th Annual International Conference on Machine Learning. ACM, 2009.
- M. Zeiler, et al. "Deconvolutional networks", Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2010.
- M. Zeiler and R. Fergus. "Visualizing and understanding convolutional networks", Computer Vision-ECCV 2014, pp. 818-833, 2014.
- C. Farabet, "Towards Real-Time Image Understanding with Convolutional Network", Diss. Universite Paris-Est, 2013.
- R. Girshick, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation", Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2014.
- J. Ba, V. Mnih, and K. Kavukcuoglu. "Multiple object recognition with visual attention", arXiv preprint arXiv:1412.7755, 2014.
- P. Sermanet, A. Frome, and E. Real. "Attention for Fine-Grained Categorization", arXiv preprint arXiv: 1412.7054, 2014.