참고문헌 (136)

1. 10.5555/2969033.2969125 

   

2. Proceedings of the International Conference on Learning Representations (ICLR) Diederik P. K. 2014 P. K. Diederik and M. Welling. 2014. Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations (ICLR). 

3. 10.1007/978-3-030-22885-9_1 

   

4. 10.5555/3045390.3045555 

   

5. T. Karras T. Aila S. Laine and J. Lehtinen. 2017. Progressive growing of GANs for improved quality stability and variation. arXiv preprint arXiv:1710.10196 . 

6. 10.5555/3157096.3157165 

   

7. Hong, Yongjun, Hwang, Uiwon, Yoo, Jaeyoon, Yoon, Sungroh. How Generative Adversarial Networks and Their Variants Work : An Overview. ACM computing surveys, vol.52, no.1, 1-43.

   상세보기

8. Jabbar, Abdul, Li, Xi, Omar, Bourahla. A Survey on Generative Adversarial Networks: Variants, Applications, and Training. ACM computing surveys, vol.54, no.8, 1-49.

   상세보기

9. Archives of Computational Methods in Engineering Alqahtani H. 1 Applications of generative adversarial networks (GANs): An updated review H. Alqahtani, M. Kavakli-Thorne, and G. Kumar. Applications of generative adversarial networks (GANs): An updated review. Archives of Computational Methods in Engineering, pp. 1-28. 

10. J. Gui Z. Sun Y. Wen D. Tao and J. Ye. 2020. A review on generative adversarial networks: Algorithms theory and applications. arXiv preprint arXiv:2001.06937 . 

11. A. Radford L. Metz and S. Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 . 

12. I. Goodfellow. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 . 

13. M. Mirza and S. Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 . 

14. 10.5555/3157096.3157340 

    

15. 10.5555/3305890.3305954 

    

16. A. Brock J. Donahue and K. Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 . 

17. 10.1109/CVPR.2019.00453 

    

18. 10.1109/CVPR42600.2020.00813 

    

19. International Conference on Machine Learning Zhang H. 7354 2019 H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena. 2019. Self-attention generative adversarial networks. In International Conference on Machine Learning, 2019, pp. 7354-7363. 

20. M. Arjovsky S. Chintala and L. Bottou. 2017. Wasserstein GAN. arXiv preprint arXiv:1701.07875 . 

21. Pan, Zhaoqing, Yu, Weijie, Yi, Xiaokai, Khan, Asifullah, Yuan, Feng, Zheng, Yuhui. Recent Progress on Generative Adversarial Networks (GANs): A Survey. IEEE access : practical research, open solutions, vol.7, 36322-36333.

    상세보기

22. Creswell, Antonia, White, Tom, Dumoulin, Vincent, Arulkumaran, Kai, Sengupta, Biswa, Bharath, Anil A.. Generative Adversarial Networks: An Overview. IEEE signal processing magazine, vol.35, no.1, 53-65.

    상세보기

23. 10.1007/978-3-030-20912-4_24 

    

24. Wang, Kunfeng, Gou, Chao, Duan, Yanjie, Lin, Yilun, Zheng, Xinhu, Wang, Fei-Yue. Generative adversarial networks: introduction and outlook. IEEE/CAA journal of automatica sinica, vol.4, no.4, 588-598.

    상세보기

25. S. Hitawala. 2018. Comparative study on generative adversarial networks. arXiv preprint arXiv:1801.04271 . 

26. Wang, Zhengwei, She, Qi, Ward, Tomás E.. Generative Adversarial Networks in Computer Vision : A Survey and Taxonomy. ACM computing surveys, vol.54, no.2, 1-38.

    상세보기

27. K. Cheng R. Tahir L. K. Eric and M. Li. An analysis of generative adversarial networks and variants for image synthesis on MNIST dataset. Multimedia Tools and Applications pp. 1-28. 

28. 10.1145/3446374 D. Saxena and J. Cao. 2020. Generative adversarial networks (GANs): Challenges solutions and future directions. arXiv preprint arXiv:2005.00065 . 

    상세보기

29. Y. LeCun C. Cortes and C. Burges. 2010. MNIST handwritten digit database. 

30. S. N. Esfahani and S. Latifi. A Survey of the State-of-the-Art GAN-based approaches to image synthesis. 

31. H. Huang P. S. Yu and C. Wang. 2018. An introduction to image synthesis with generative adversarial nets. arXiv preprint arXiv:1803.04469 . 

32. Wu, Xian, Xu, Kun, Hall, Peter. A survey of image synthesis and editing with generative adversarial networks. Tsinghua science and technology. = 淸華大淸華大學學報.自然科學版, vol.22, no.6, 660-674.

    상세보기

33. Cao, Yang-Jie, Jia, Li-Li, Chen, Yong-Xia, Lin, Nan, Yang, Cong, Zhang, Bo, Liu, Zhi, Li, Xue-Xiang, Dai, Hong-Hua. Recent Advances of Generative Adversarial Networks in Computer Vision. IEEE access : practical research, open solutions, vol.7, 14985-15006.

    상세보기

34. 10.1002/widm.1345 J. Agnese J. Herrera H. Tao and X. Zhu. 2019. A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis. arXiv preprint arXiv:1910.09399 . 

    상세보기

35. H. Xiao K. Rasul and R. Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 . 

36. Medical ImageAanalysis Yi X. 101552 2019 Generative adversarial network in medical imaging: A review X. Yi, E. Walia, and P. Babyn. 2019. Generative adversarial network in medical imaging: A review. Medical ImageAanalysis, p. 101552. 

37. Sorin, Vera, Barash, Yiftach, Konen, Eli, Klang, Eyal. Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review. Academic radiology, vol.27, no.8, 1175-1185.

    상세보기

38. F. Di Mattia P. Galeone M. De Simoni and E. Ghelfi. 2019. A survey on GANs for anomaly detection. arXiv preprint arXiv:1906.11632 . 

39. International Journal of Computer Applications Torres-Reyes N. 8887 975 Audio enhancement and synthesis using generative adversarial networks: A survey N. Torres-Reyes and S. Latifi. Audio enhancement and synthesis using generative adversarial networks: A survey. International Journal of Computer Applications, vol. 975, p. 8887. 

40. Artificial Intelligence Review Yinka-Banjo C. 1 2019 A review of generative adversarial networks and its application in cybersecurity C. Yinka-Banjo and O.-A. Ugot. 2019. A review of generative adversarial networks and its application in cybersecurity. Artificial Intelligence Review, pp. 1-16. 

41. 10.1109/CVPR.2019.00649 

    

42. 10.1109/CVPR.2018.00165 

    

43. B. Duan W. Wang H. Tang H. Latapie and Y. Yan. 2019. Cascade attention guided residue learning GAN for Cross-Modal translation. arXiv preprint arXiv:1907.01826 . 

44. 10.5555/3504035.3504900 

    

45. X. Sun H. Xu and K. Saenko. 2018. A two-stream variational adversarial network for video generation. arXiv preprint arXiv:1812.01037 . 

46. IEEE Transactions on Geoscience and Remote Sensing Liu Q. 2020 PSGAN: A generative adversarial network for remote sensing image pan-sharpening Q. Liu, H. Zhou, Q. Xu, X. Liu, and Y. Wang. 2020. PSGAN: A generative adversarial network for remote sensing image pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing. 

47. IEEE Transactions on Geoscience and Remote Sensing Yu W. 2021 Convolutional two-stream generative adversarial network-based hyperspectral feature extraction W. Yu, M. Zhang, Z. He, and Y. Shen. 2021. Convolutional two-stream generative adversarial network-based hyperspectral feature extraction. IEEE Transactions on Geoscience and Remote Sensing. 

48. A. Clark J. Donahue and K. Simonyan. 2019. Efficient video generation on complex datasets. arXiv preprint arXiv:1907.06571 . 

49. 10.1007/s11263-019-01251-8 K. Vougioukas S. Petridis and M. Pantic. 2018. End-to-end speech-driven facial animation with temporal GANs. arXiv preprint arXiv:1805.09313 . 

    상세보기

50. 10.1109/WACV45572.2020.9093527 

    

51. 10.5555/3326943.3327049 

    

52. 10.5555/3326943.3327049 

    

53. Q. Hu A. Waelchli T. Portenier M. Zwicker and P. Favaro. 2018. Video synthesis from a single image and motion stroke. arXiv preprint arXiv:1812.01874 . 

54. 10.1109/ICCV.2017.308 

    

55. 10.5555/3367243.3367316 

    

56. 10.5555/3504035.3504326 

    

57. M. Saito and S. Saito. 2018. TGANv2: Efficient training of large models for video generation with multiple subsampling layers. arXiv preprint arXiv:1811.09245 . 

58. 10.1109/CVPR42600.2020.00531 

    

59. 10.1007/978-3-030-01234-2_32 

    

60. Zhou, Hang, Liu, Yu, Liu, Ziwei, Luo, Ping, Wang, Xiaogang. Talking Face Generation by Adversarially Disentangled Audio-Visual Representation. Proceedings of the ... aaai conference on artificial intelligence, vol.33, 9299-9306.

    상세보기

61. S. A. Jalalifar H. Hasani and H. Aghajan. 2018. Speech-driven facial reenactment using conditional generative adversarial networks. arXiv preprint arXiv:1803.07461 . 

62. 10.1145/3123266.3127905 

    

63. 10.1109/CVPR.2019.00385 

    

64. 10.1109/ICCV.2019.00916 

    

65. 10.1007/978-3-030-58517-4_31 

    

66. M. Mathieu C. Couprie and Y. LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 . 

67. A. X. Lee R. Zhang F. Ebert P. Abbeel C. Finn and S. Levine. 2018. Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523 . 

68. 10.1109/CVPR.2018.00251 

    

69. 10.1007/978-3-030-01216-8_23 

    

70. 10.5555/3294771.3294816 

    

71. R. Villegas J. Yang S. Hong X. Lin and H. Lee. 2017. Decomposing motion and content for natural video sequence prediction. arXiv preprint arXiv:1706.08033 . 

72. 10.1109/ICCV.2017.361 

    

73. 10.1109/ICCV.2017.194 

    

74. 10.5555/3294996.3295195 

    

75. 10.1109/CVPR.2019.00824 

    

76. 10.1109/ICCV.2019.00125 

    

77. 10.1109/ICCV.2019.00603 

    

78. 10.1109/ICCVW.2019.00153 Y. Zhou Z. Wang C. Fang T. Bui and T. L. Berg. 2019. Dance dance generation: Motion transfer for internet videos. arXiv preprint arXiv:1904.00129 . 

    

79. 10.1109/CVPR42600.2020.00535 

    

80. Kim, Hyeongwoo, Garrido, Pablo, Tewari, Ayush, Xu, Weipeng, Thies, Justus, Niessner, Matthias, Pérez, Patrick, Richardt, Christian, Zollhöfer, Michael, Theobalt, Christian. Deep video portraits. ACM transactions on graphics, vol.37, no.4, 1-14.

    상세보기

81. 10.1109/CVPR.2019.00248 

    

82. Liu, Lingjie, Xu, Weipeng, Zollhöfer, Michael, Kim, Hyeongwoo, Bernard, Florian, Habermann, Marc, Wang, Wenping, Theobalt, Christian. Neural Rendering and Reenactment of Human Actor Videos. ACM transactions on graphics, vol.38, no.5, 1-14.

    상세보기

83. O. Gafni L. Wolf and Y. Taigman. 2019. Vid2game: Controllable characters extracted from real-world videos. arXiv preprint arXiv:1904.08379 . 

84. 10.1007/978-3-030-01246-5_37 

    

85. 10.1109/CVPR46437.2021.00505 

    

86. L. Li J. Bao H. Yang D. Chen and F. Wen. 2019. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457 . 

87. 10.1007/978-3-030-01228-1_8 

    

88. 10.1109/ICCV.2017.244 

    

89. 10.1007/978-3-030-00928-1_60 

    

90. 10.1109/ICASSP.2018.8462614 

    

91. 10.1109/CVPR.2017.632 

    

92. 10.1109/CVPR.2016.278 

    

93. 10.1007/978-3-319-46475-6_43 

    

94. 10.1109/CVPR.2018.00917 

    

95. K. Soomro A. R. Zamir and M. Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 . 

96. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.. Gradient-based learning applied to document recognition. Proceedings of the IEEE, vol.86, no.11, 2278-2324.

    상세보기

97. 10.5555/1018429.1020906 

    

98. Y. Balaji M. R. Min B. Bai R. Chellappa and H. P. Graf. 2018. TFGAN: Improving conditioning for Text-to-Video synthesis. 

99. Cooke, Martin, Barker, Jon, Cunningham, Stuart, Shao, Xu. An audio-visual corpus for speech perception and automatic speech recognition. The Journal of the Acoustical Society of America, vol.120, no.5, 2421-2424.

    상세보기

100. Asian Conference on Computer Vision Chung J. S. 87 2016 J. S. Chung and A. Zisserman. 2016. Lip reading in the wild. In Asian Conference on Computer Vision, 2016: Springer, pp. 87-103. 

101. Ionescu, Catalin, Papava, Dragos, Olaru, Vlad, Sminchisescu, Cristian. Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments. IEEE transactions on pattern analysis and machine intelligence, vol.36, no.7, 1325-1339.

     상세보기

102. 10.1109/ICCV.2005.28 

     

103. International Conference on Information Technology and Applications (ICITA) Alqahtani H. 2019 H. Alqahtani, M. Kavakli-Thorne, G. Kumar, and F. SBSSTC. 2019. An analysis of evaluation metrics of GANs. In International Conference on Information Technology and Applications (ICITA). 

104. 10.5555/3157096.3157346 

     

105. 10.5555/3295222.3295408 

     

106. 10.1109/ACSSC.2003.1292216 

     

107. Zhou Wang, Bovik, A.C.. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE signal processing magazine, vol.26, no.1, 98-117.

     상세보기

108. Horé, Alain, Ziou, Djemel. Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure?. IET image processing, vol.7, no.1, 12-24.

     상세보기

109. 10.5555/3042817.3043083 

     

110. 10.1145/3126686.3126723 

     

111. 10.5555/2586117.2587158 

     

112. Harte, Naomi, Gillen, Eoin. TCD-TIMIT: An Audio-Visual Corpus of Continuous Speech. IEEE transactions on multimedia, vol.17, no.5, 603-615.

     상세보기

113. 10.5555/3172077.3172168 

     

114. F. Ebert C. Finn A. X. Lee and S. Levine. 2017. Self-supervised visual planning with temporal skip connections. arXiv preprint arXiv:1710.05268 . 

115. A. Rössler D. Cozzolino L. Verdoliva C. Riess J. Thies and M. Nießner. 2018. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179 . 

116. 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10 Aifanti N. 1 2010 N. Aifanti, C. Papachristou, and A. Delopoulos. 2010. The MUG facial expression database. In 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, 2010: IEEE, pp. 1-4. 

117. 10.1109/CVPR.2014.223 

     

118. A. Gorban et al. 2015. THUMOS challenge: Action recognition with a large number of classes. ed. 

119. 10.5555/1896300.1896315 

     

120. 10.1109/CVPR.2017.28 

     

121. Audiovisual Database of Spoken American English Richie C. 2009 C. Richie, S. Warburton, and M. Carter. 2009. Audiovisual Database of Spoken American English. Linguistic Data Consortium. 

122. 10.1109/CVPR.2017.492 

     

123. Houwei Cao, Cooper, David G., Keutmann, Michael K., Gur, Ruben C., Nenkova, Ani, Verma, Ragini. CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset. IEEE transactions on affective computing, vol.5, no.4, 377-390.

     상세보기

124. T. Afouras J. S. Chung and A. Zisserman. 2018. LRS3-TED: A large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496 . 

125. Geiger, A, Lenz, P, Stiller, C, Urtasun, R. Vision meets robotics: The KITTI dataset. The International journal of robotics research, vol.32, no.11, 1231-1237.

     상세보기

126. Computer Vision and Pattern Recognition (CVPR) Schiele B. 2009 B. Schiele, P. Dollár, C. Wojek, and P. Perona. 2009. Pedestrian detection: A benchmark. In Computer Vision and Pattern Recognition (CVPR). 

127. Dibeklioglu, H., Salah, A.A., Gevers, T.. Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles. Lecture notes in computer science, vol.7574, 525-538.

     상세보기

128. 10.1109/ICCV.2017.243 

     

129. 10.1109/CVPR.2016.350 

     

130. 10.1109/CVPRW.2018.00141 

     

131. 10.1145/3123266.3123309 

     

132. 10.5555/2002472.2002497 

     

133. N. Xu et al. 2018. Youtube-vos: A large-scale video object segmentation benchmark. arXiv preprint arXiv:1809.03327 . 

134. S. Caelles et al. 2018. The 2018 Davis challenge on video object segmentation. arXiv preprint arXiv:1803.00557 . 

135. videvo. “videvo.” https://www.videvo.net/(accessed 2021). 

136. 10.1109/CVPR.2009.5206557