인간 수준에 근접한 딥러닝 기반 영상 인식의 동향

  • Published : 2015.09.18

Abstract

Keywords

References

  1. Frank Rosenblatt, "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms", 1962.
  2. Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams, "Learning representations by back-propagating errors, " Nature 323.6088 (1986): 533-536. https://doi.org/10.1038/323533a0
  3. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Handwritten digit recognition with a back-propagation network, in Touretzky, David (Eds), Advances in Neural Information Processing Systems, NIPS 1989.
  4. Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
  5. Lee, Honglak, Chaitanya Ekanadham, and Andrew Ng. "Sparse deep belief net model for visual area V2." Advances in neural information processing systems. 2007.
  6. Lee, Honglak, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations." Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009.
  7. Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, and A. Ng. Building high-level features using large scale unsupervised learning. International Conference on Machine Learning, 2012.
  8. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", NIPS 2012.
  9. Matthew D. Zeiler and Rob Fergus, "Visualizing and Understanding Convolutional Networks", Arxiv 1311.2901 (Nov 28, 2013)
  10. C.Szegedy, W. Liu, Y Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper with Convolutions, " CVPR 2015.
  11. K. Simonyan and A. Zissennan, "Very Deep Convolutional Networks for Large-Scale Image Recognition, " ICLR 2015.
  12. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification, " IEEE Conference on Computer Vision and Pattern Recognition, pp. 1,701-1,708, Jun., 2014.
  13. Y. Sun, X. Wang, and X. Tang, "Deep Learning Face Representation from Predicting 10,000 classes, " IEEE Conference on Computer Vision and Pattern Recognition, pp. 1,891-1,898, Jun., 2014.
  14. Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep Learning Face Representation by Joint Identification-Verification," Neural Information Processing Systems, pp. 1,988-1,996, 2014.
  15. F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering, " IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.
  16. E. Zhou, Z. Cao, and Q. Yin, "Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?" arXiv, arXiv:1501.04690, Jan., 2015.
  17. X. Xiong, F. De la Torre, "Supervised descent method and its applications to face alignment", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
  18. Y. Sun, X. Wang, and X. Tang, "Deep convolutional network cascade for facial point detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
  19. J. Zhang, S. Shan, M. Kan, and X. Chen, "Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment", ECCV 2014.
  20. Z. Zhang, P. Luo, C. C. Loy, and X. Tang, "Facial Landmark Detection by Deep Multi-task Learning", European Conference on Computer Vision (ECCV), 2014.
  21. http://deeplearning.net/
  22. https://en.wikipedia.org/wiki/Internet_of_Things
  23. 김대수, 신경망 이론과 응용, 하이테크 정보, 1992, Page 59-86