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| 1 | +# 5. 참고 문헌 |
| 2 | + |
| 3 | +## Dataset |
| 4 | + |
| 5 | +- [Victorian400 Dataset for Colorizing Victorian Illustrations](https://www.kaggle.com/elibooklover/victorian400) |
| 6 | + |
| 7 | +## Papers |
| 8 | + |
| 9 | +- [Pros and Cons of GAN Evaluation Measures](https://doi.org/10.1016/j.cviu.2018.10.009) |
| 10 | +- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) |
| 11 | +- [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) |
| 12 | +- [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004) |
| 13 | +- [Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784) |
| 14 | +- [Generative Adversarial Nets 분석과 적용사례](https://www.koreascience.or.kr/article/CFKO201736257096695.pdf) |
| 15 | +- [Synthesizing Obama: Learning Lip Sync from Audio](https://grail.cs.washington.edu/projects/AudioToObama/siggraph17_obama.pdf) |
| 16 | +- [An_Analysis_of_Evaluation_Metrics_of_GANs](https://www.researchgate.net/publication/337876790_AN_ANALYSIS_OF_EVALUATION_METRICS_OF_GANS) |
| 17 | +- [Progressive_Growing_of_GANs_for_Improved_Quality_Stability_and_Variation](https://arxiv.org/abs/1710.10196) |
| 18 | +- [Eye In-Painting with Exemplar Generative Adversarial Networks](https://arxiv.org/abs/1712.03999) |
| 19 | +- [Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News](https://journals.sagepub.com/doi/full/10.1177/2056305120903408) |
| 20 | +- [Improved Techniques For Training GANs](https://arxiv.org/abs/1606.03498) |
| 21 | +- [On the Evaluation of Conditional GANs](https://arxiv.org/abs/1907.08175) |
| 22 | + |
| 23 | +## Code |
| 24 | + |
| 25 | +- [PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/gan/gan.py) |
| 26 | +- [GAN_colorization](https://github.com/hichoe95/GAN_colorization) |
| 27 | +- [clearing-a-subplot-in-matplotlib](https://stackoverflow.com/questions/47282918/clearing-a-subplot-in-matplotlib) |
| 28 | +- [row-and-column-headers-in-matplotlibs-subplots](https://stackoverflow.com/questions/25812255/row-and-column-headers-in-matplotlibs-subplots) |
| 29 | +- [matplotlib-subplots-get-rid-of-tick-labels-altogether](https://stackoverflow.com/questions/25124143/matplotlib-subplots-get-rid-of-tick-labels-altogether) |
| 30 | +- [matplotlib-imshow-stretch-to-fit-width](https://stackoverflow.com/questions/12806481/matplotlib-imshow-stretch-to-fit-width) |
| 31 | + |
| 32 | + |
| 33 | +## Blog |
| 34 | + |
| 35 | + |
| 36 | +- [쉽게 씌어진 GAN](https://dreamgonfly.github.io/blog/gan-explained/) |
| 37 | +- [cCAN(conditionial GAN)](https://blog.naver.com/laonple/221306150417) |
| 38 | +- [CGAN](https://leechamin.tistory.com/229) |
| 39 | +- [Best Resources for Getting Started With GANs](https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/) |
| 40 | + |
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