References
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel distributed processing: explorations in the microstructure of cognition, Vol. 1, pp. 318-362, 1986.
- Simon Haykin, Neural Networks: A Comprehensive Foundation (2 ed.). Prentice Hall. 1994.
- N. Dalal and B. Triggs, "Histograms of Oliented gradients for human detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, pp. 886-893, June 2005.
- D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the International Conference on Computer Vision, pp. 1150-1157, 1999.
- D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision (IJCV), 60(2):91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
- T. Ojala, M. Pietikainen, and D. Harwood, "A Comparative Study of Texture Measures with Classification Based on Feature Distributions," Pattern Recognition, 29(1):51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
- Fang Zheng, Guoliang Zhang, and Zhanjiang Song, "Comparison of Different Implementations of MFCC," Journal of Computer Science and Technology, 16(6): 582-589, 2001. https://doi.org/10.1007/BF02943243
- Li Deng, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael L. Seltzer, Geoffrey Zweig, Xiaodong He, Jason Williams, Yifan Gong, and Alex Acero, "Recent advances in deep learning for speech research at Microsoft," in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 8604-8608, May 2013.
- Li Deng and Dong Yu, "Deep Learning: Methods and Applications," Foundations and Trends in Signal Processing, vol. 7(3-4), pp. 197-387, 2014. https://doi.org/10.1561/2000000039
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, 86(11):2278-2324, November 1998. https://doi.org/10.1109/5.726791
- Yangqing Jia, "Caffe: An open source convolutional architecture for fast feature embedding," http://caffe.berkeleyvision.org/, 2013.
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell, "Caffe: Convolutional Architecture for Fast Feature Embedding," arXiv:1408.5093, 2014.
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems, 25, pp. 1097-1105, 2012.
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision (IJCV), pp. 1-42, April 2015.
- Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio, "Maxout Networks," Journal of Machine Learning Research W&CP, 28 (3): 1319-1327, 2013.
- http://symas.com/mdb/, LMDB Reference Guide, 19 Oct 2014.
- Code.google.com, "ThirdPartyAddOns-protobuf-Links to third-party add-ons,"Google Project Hosting, 7 Nov 2012.
- R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, "Going Deeper with Convolutions," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.