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코사인 유사도에 기반한 적대 과정 딥러닝의 개선안과 화자인식 및 도메인적응에의 응용 = Advanced adversarial deep learning process based on cosine similarity, and its applications to speaker recognition and domain adaptation

허희수 2019년
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논문상세정보
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코사인 유사도에 기반한 적대 과정 딥러닝의 개선안과 화자인식 및 도메인적응에의 응용 = A..
' 코사인 유사도에 기반한 적대 과정 딥러닝의 개선안과 화자인식 및 도메인적응에의 응용 = Advanced adversarial deep learning process based on cosine similarity, and its applications to speaker recognition and domain adaptation' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 도메인 적응
  • 딥 러닝
  • 이미지 인식
  • 적대 과정
  • 코사인유사도
  • 화자 식별
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 코사인 유사도에 기반한 적대 과정 딥러닝의 개선안과 화자인식 및 도메인적응에의 응용 = Advanced adversarial deep learning process based on cosine similarity, and its applications to speaker recognition and domain adaptation' 의 참고문헌

  • Y. Shinohara, “Adversarial Multi-Task Learning of Deep Neural Networks for Robust Speech Recognition.,” in INTERSPEECH, 2016, pp. 2369–2372.
  • Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS workshop on deep learning and unsupervised feature learning, 2011, vol. 2011, no. 2, p. 5.
  • Y. LeCun and C. Cortes, “{MNIST} handwritten digit database,” 2010.
  • Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” J. Mach. Learn. Res., vol. 17, no. 1, pp. 2030–2096, 2016.
  • Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798–1828, 2013.
  • X. Wu, R. He, Z. Sun, and T. Tan, “A light cnn for deep face representation with noisy labels,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 11, pp. 2884–2896, 2018.
  • X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp. 249–256.
  • X. Gastaldi, “Shake-shake regularization,” arXiv Prepr. arXiv1705.07485, 2017.
  • S. Zagoruyko and N. Komodakis, “Wide residual networks,” arXiv Prepr. arXiv1605.07146, 2016.
  • S. J. Reddi, S. Kale, and S. Kumar, “On the convergence of adam and beyond,” 2018.
  • S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in International Conference on Machine Learning, 2015, pp. 448–456.
  • N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Trans. Audio. Speech. Lang. Processing, vol. 19, no. 4, pp. 788– 798, 2011.
  • Martin~Abadi, Ashish~Agarwal, Paul~Barham, Eugene~Brevdo, Zhifeng~Chen, Craig~Citro, Greg~S.~Corrado, Andy~Davis, Jeffrey~Dean, Matthieu~Devin, Sanjay~Ghemawat, Ian~Goodfellow, Andrew~Harp, Geoffrey~Irving, Michael~Isard, Y. Jia, Rafal~Jozefowicz, Lukasz~Kaiser, Manjunath~Kudlur, Josh~Levenberg, Dan~Man , Rajat~Monga, Sherry~Moore, Derek~Murray, Chris~Olah, Mike~Schuster, Jonathon~Shlens, Benoit~Steiner, Ilya~Sutskever, Kunal~Talwar, Paul~Tucker, Vincent~Vanhoucke, Vijay~Vasudevan, Fernanda~Vi gas, Oriol~Vinyals, Pete~Warden, Martin~Wattenberg, Martin~Wicke, Yuan~Yu, and Xiaoqiang~Zheng, “{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems.” 2015.
  • M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv Prepr. arXiv1411.1784, 2014.
  • L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, no. Nov, pp. 2579–2605, 2008.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision, 2016, pp. 630–645.
  • 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, 2016, pp. 770–778.
  • I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,” arXiv Prepr. arXiv1608.03983, 2016.
  • I. J. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, and D. Warde-farley, “Generative Adversarial Nets,” pp. 1–9.
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
  • H. Yu, Z.-H. Tan, Z. Ma, and J. Guo, “Adversarial network bottleneck features for noise robust speaker verification,” arXiv Prepr. arXiv1706.03397, 2017.
  • F. Chollet and others, “Keras.” GitHub, 2015.
  • E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Computer Vision and Pattern Recognition (CVPR), 2017, vol. 1, no. 2, p. 4.
  • E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial Discriminative Domain Adaptation.”
  • D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, and others, “The Kaldi speech recognition toolkit,” in IEEE 2011 workshop on automatic speech recognition and understanding, 2011, no. EPFL-CONF-192584.
  • D. P. Kingma and J. L. Ba, “Adam: Method for Stochastic Optimization,” in International Conference on Learning Representation, 2015.
  • D. L. Poole, A. K. Mackworth, and R. Goebel, Computational intelligence: a logical approach, vol. 1. Oxford University Press New York, 1998.
  • C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
  • B. Xu, N. Wang, T. Chen, and M. Li, “Empirical evaluation of rectified activations in convolutional network,” arXiv Prepr. arXiv1505.00853, 2015.
  • A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 2011, pp. 1521–1528.
  • A. Solomonoff, C. Quillen, and W. M. Campbell, “Channel compensation for SVM speaker recognition.,” in Odyssey, 2004, vol. 4, pp. 219–226.
  • A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv Prepr. arXiv1511.06434, 2015.
  • A. M. Saxe, J. L. McClelland, and S. Ganguli, “Exact solutions to the nonlinear dynamics of learning in deep linear neural networks,” arXiv Prepr. arXiv1312.6120, 2013.
  • A. Larcher, K. A. Lee, B. Ma, and H. Li, “Text-dependent speaker verification: Classifiers, databases and RSR2015,” Speech Commun., vol. 60, pp. 56–77, 2014.
  • A. Larcher, K. A. Lee, B. Ma, and H. Li, “Rsr2015: Database for text-dependent speaker verification using multiple pass-phrases,” in Thirteenth Annual Conference of the International Speech Communication Association, 2012.
  • A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009.