과제정보
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NO.2017R1D1A1B04030870).
참고문헌
- E.L. Delton, S. Chintala, and R. Fergus, "Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks", In Advances Neural Information Processing Systems, pp. 1486-1494, 2015.
- H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 8, pp. 1947-1962, August 2018. https://doi.org/10.1109/tpami.2018.2856256
- T. Karras, T. Aila, S. Laine, and J. Lehtinen, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", arXiv preprint arXiv:1710.10196, 2017.
- B. J. Kim, "Improved Deep Learning Algorithm", Journal of Advanced Information Technology and Convergence, Vol. 8, No. 2, pp. 119-127, December 2018. https://doi.org/10.14801/jaitc.2018.8.2.119
- C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, "Intriguing Properties of Neural Networks", arXiv preprint arXiv: 1312.6199, 2013.
- S. Gu., and L. Rigazio, "Towards Deep Neural Network Architectures Robust to Adversarial Examples", arXiv preprint arXiv: 1412.5068, 2014.
- D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, "Context Encoders: Feature Learning by Inpainting", In CVPR, 2016.
- C. Yang, X. Lu, Z. Lin, E. Shechtman, O. Wang, and H. Li, "High-resolution Image Inpainting using Multi-scale Neural Patch Synthesis", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6721-6729.
- J. M. Lee, and D. S. Kang, "Optimizing Data Augmentation Based Few-shot Learning Method for Improving Indoor Fire Detection Accuracy", Proceedings of KIIT Conference, June 2019, pp. 237-240.
- H. Biao, and D.S. Kang, "The Research of Face Expression Recognition based on CNN using Tensorflow", Journal of Advanced Information Technology and Convergence, Vol. 7, No. 1, pp. 55-63, July 2017. https://doi.org/10.14801/jaitc.2017.7.1.55
- A. Odena, C. Olah, and J. Shlens, "Conditional Image Synthesis with Auxiliary Classifier GANs", In Proceedings of the 34th International Conference on Machine Learning, JMLR, Vol. 70, Sydney, Australia, August 2017, pp. 2642-2651.
- A. Radford, L. Metz, and S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial networks", arXiv preprint arXiv:1511.06434, 2015.
- W. Fedus, I. Goodfellow, and A. M. Dai, "MaskGAN: Better Text Generation via Filling in the", arXiv preprint arXiv:1801.07736, 2018.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
- A. Brock, J. Donahue, and K. Simonyan, "Large Scale GAN Training for High Fidelity Natural Image Synthesis", arXiv preprint arXiv:1809.11096, 2018.
- M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein GAN", arXiv preprint arXiv:1701. 07875, 2017.
- I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dubmoulin, and A. Courville, "Improved Training of Wasserstein GANs", arXiv preprint arXiv:1704.00028, 2017.
- C. Nebauer, "Evaluation of Convolutional Neural Networks for Visual Recognition", IEEE Transactions on Neural Networks, Vol. 9, No. 4, pp. 685-696, July 1998. https://doi.org/10.1109/72.701181
- A. Krizhevsky, "Convolutional Deep Belief Network on CIFAR-10", 2010.
- J.Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks", In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, July 2017, pp. 2223-2232.