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참고문헌 (15)

1. K. Ganguly, Learning Generative Adversarial Networks, PACKT, Oct. 2017. 

2. A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. Bharath, "Generative Adversarial Networks: An Overview", Arxiv, [Online]. Available: https://arxiv.org/pdf/1710.07035.pdf, Oct. 2017. 

3. Eindhoven University of Technology, "Efficient Mapping of the Training of Convolutional Neural Networks to a CUDA-based cluster." [Online]. Available: http://parse.ele.tue.nl/education/cluster2. 

4. Z. C. Lipton, J. Berkowitz, and C. Elkan, "A Critical Review of Recurrent Neural Networks for Sequence Learning", [Online]. Available: https://arxiv.org/pdf/1506.00019.pdf, June 2015. 

5. J. T. Connor, R. D. Martin, and L. E. Atlas, "Recurrent Neural Networks and Robust Time Series Prediction", IEEE Trans. Neural Netw., vol. 5, no. 2, pp. 240-254, Feb. 1994. 

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6. T. Mikolov, I. Sutsker, K. Chen, G. Corrado, J. Dean, "Distributed Representations of Words and Phrases and their Compositionality", Neural Information Processing Systems Conference, 2013 

7. Dev Nag, "Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)", [Online]. Available: https://medium.com/@devnag/generative-adversarialnetworks-gans-in-50-lines-of-code-pytorch-e81b79659e3f, Feb. 2017. 

8. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative Adversarial Networks", Neural Information Processing Systems Conference (NIPS), 2014 

9. C. Ledig, L. Theis, F. Huszar, J. Caballero, and A. Cunningham, "Photo-Realistic Single Image Super- Resolution Using a Generative Adversarial Network", Neural Information Processing Systems Conference (NIPS), 2016 

10. A. Radford and L. Metz, "Unsupervised representation learning with deep convolutional generative adversarial networks", International Conference on Learning Representations, Jan. 2016 

11. S. Reed, Z. Akata, X. Yan, and L. Logeswaran, "Generative adversarial text to image synthesis" 

12. T. White, "Sampling generative networks", Arxiv, [Online]. Available: https://arxiv.org/abs/1609.04468, Sept. 2016. 

13. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, "Improved Techniques for Training GANs", Neural Information Processing Systems Conference (NIPS), 2016 

14. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets", Arxiv, [Online]. https://arxiv.org/abs/1606.03657, June 2016. 

15. B. Hitaj, G. Ateniese, F. Perez-Cruz, "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning", Arxiv, [Online]. Available: https://arxiv.org/abs/1702.07464, Feb. 2017