Understanding Trending Variants of Generative Adversarial Networks


  • Tanvi Bhandarkar
  • A Murugan






Generative Adversarial Networks (GANs), generative models, adversarial learning.


Generative Adversarial Networks (GAN) have its major contribution to the field of Artificial Intelligence. It is becoming so powerful by paving its way in numerous applications of intelligent systems. This is primarily due to its astute prospect of learning and solving complex and high-dimensional problems from the latent space. With the growing demands of GANs, it is necessary to seek its potential and impact in implementations. In short span of time, it has witnessed several variants and extensions in image translation, domain-adaptation and other academic fields. This paper provides an understanding of such imperative GANs mutants and surveys the existing adversarial models which are prominent in their applied field.



[1] Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Advances in Neural Information Processing Systems 27, Montreal, Quebec, Canada, 2014, pp. 26722680.

[2] H. K. Wu, S. Zheng, J. G. Zhang, and K. Q. Huang, GP-GAN: Towards Realistic High-Resolution Image Blending, arXiv preprint arXiv: 1703.07195, 2017.

[3] R. A. Yeh, C. Chen, T. Y. Lim, A. G. Schwing, M. Hasegawa-Johnson, and M. N. Do, Semantic image inpainting with deep generative models, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR),Honolulu, HI, USA, 2017

[4] W. Y. Wang, Q. G. Huang, S. Y. You, C. Yang, and U. Neumann, Shape inpainting using 3D generative adversarial network and recurrent convolutional networks, in The IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 22982306.

[5] Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks , in Int. Conf. Learning Representations (ICLR), San Juan, Puerto Rico, 2016.

[6] Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:448-456, 2015.

[7] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao, LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop, in arXiv:1506.03365 [cs.CV], 2016.

[8] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, Generative Adversarial Text to Image Synthesis, arXiv preprint arXiv:1605.05396, 2016.

[9] Mirza, M. and Osindero, S., Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784, 2014.

[10] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, arXiv preprint arXiv:1609.04802, 2016.

[11] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 19, 2015.

[12] K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), 2015.

[13] Denton, Emily, Chintala, Soumith, Szlam, Arthur, and Fergus, Rob, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, arXiv preprint arXiv:1506.05751, 2015.

[14] P. J. Burt, Edward, and E. H. Adelson, The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31:532540, 1983.

[15] D. Berthelot, T. Schumm, and L. Metz, Began: Boundary equilibrium generative adversarial networks, arXiv preprint arXiv:1703.10717, 2017.

[16] J. Zhao, M. Mathieu, and Y. LeCun. Energy-based generative adversarial network. In 5th International Conference on Learning Representations (ICLR), 2017.

[17] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, Generative adversarial text to image synthesis, arXiv preprint arXiv:1605.05396, 2016.

[18] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, Microsoft COCO: Common objects in context. In ECCV. 2014.

[19] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.

[20] M.-E. Nilsback and A. Zisserman, Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008.

[21] Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A.,and Clune, J. (2016), Plug & play generative networks: Conditional iterative generation of images in latent space. arXiv preprint arXiv:1612.00005.

[22] H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas, Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint arXiv:1612.03242, 2016.

[23] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks.In CVPR, 2017.

[24] 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 (ICCV), 2017.

[25] Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, StarGAN: Unified Generative Adversarial Networks for MultiDomain Image-to-Image Translation, ArXiv e-prints, Nov. 2017.

[26] M. Li, W. Zuo, and D. Zhang. Deep identity-aware transfer of facial attributes. arXiv preprint arXiv:1610.05586, 2016.

[27] G. Perarnau, J. van de Weijer, B. Raducanu, and J. M. A lvarez. Invert-ible conditional gans for image editing.arXiv preprint arXiv:1611.06355, 2016.

[28] C. Vondrick, H. Pirsiavash, and A. Torralba. Generating videos with scene dynamics. In Neural Information Processing Systems (NIPS). 2016.

[29] Tulyakov, S.; Liu, M.; Yang, X.; and Kautz, J. 2017. MoCoGAN: Decomposing motion and content for video generation. arXiv preprint arXiv:1707.04993.

[30] M. Saito, E. Matsumoto, and S. Saito. Temporal generative adversarial nets with singular value clipping. In IEEE International Conference on Computer Vision (ICCV), 2017.

[31] D. Nie, R. Trullo, C. Petitjean, S. Ruan, and D. Shen, Medical image synthesis with context-aware generative adversarial networks, arXiv preprint arXiv:1612.05362, 2016.

[32] Zhuowen Tu, Auto-context and Its Application to High-level Vision Tasks, Computer Vision and Pattern Recognition, CVPR 2008.

[33] Yuan Xue, Tao Xu, Han Zhang, Rodney Long, and Xiaolei Huang. Segan: Adversarial network with multi-scale L1 loss for medical image segmentation. arXiv preprint arXiv:1706.01805, 2017.

[34] Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S Kevin Zhou, Zhoubing Xu, JinHyeong Park, Mingqing Chen, Trac D Tran, et al. Automatic vertebra labeling in large-scale 3d ct using deep image-to-image network with message passing and sparsity regularization, In International Conference on Information Processing in Medical Imaging, pages 633644. Springer, 2017.

[35] Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, and Eric P Xing, Scan: Structure correcting adversarial network for chest x-rays organ segmentation, arXiv preprint arXiv:1703.08770, 2017.

[36] Olof Mogren, C-rnn-gan: Continuous recurrent neural networks with adversarial training, arXiv preprint arXiv:1611.09904, 2016.

[37] Sepp Hochreiter and Jrgen Schmidhuber. Long short-term memory. Neural computation, 9 (8):17351780, 1997.

[38] Li-Chia Yang, Szu-Yu Chou, and Yi-Hsuan Yang, MidiNet: A convolutional generative adversarial network for symbolic-domain music generation using 1d and 2d conditions, arXiv preprint arXiv:1703.10847, 2017.

[39] Elliot Waite, Douglas Eck, Adam Roberts, and Dan Abolafia. Project Magenta: Generating longterm structure in songs and stories, 2016. https://magenta.tensorflow.org/blog/2016/07/15/lookback-rnn-attentio n-rnn/.

[40] Michela Paganini, Luke de Oliveira, and Benjamin Nachman. Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks. arXiv preprint arXiv:1705.02355, 2017.

[41] Luke de Oliveira, Michela Paganini, and Benjamin Nachman. Learning particle physics by example: Location-aware generative adversarial networks for physics synthesis. arXiv preprint arXiv:1701.05927, 2017.

[42] Santiago Pascual, Antonio Bonafonte, and Joan Serr. Segan: Speech enhancement genera- tive adversarial network. arXiv preprint arXiv:1703.09452, 2017.

[43] Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. Adversarial ranking for language generation. arXiv preprint arXiv:1705.11001, 2017

[44] Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, and Hsin-Min Wang. Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks.arXiv preprint arXiv:1704.00849, 2017.

[45] Haichao Shi, Jing Dong, Wei Wang, Yinlong Qian, and Xiaoyu Zhang, Ssgan: Secure steganography based on generative adversarial networks.arXiv preprint arXiv:1707.01613, 2017.

[46] Emily Denton, Sam Gross, and Rob Fergus. Semi-supervised learning with context- conditional generative adversarial networks. arXiv preprint arXiv:1611.06430, 2016.

[47] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou , and Alexei Efros. Image-to- image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004, 2016.

[48] Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jungkwon Lee, and Jiwon Kim. Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192, 2017.

[49] Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, and Weiran He. Genegan: Learning object transfiguration and attribute subspace from unpaired data. arXiv preprint arXiv:1705.04932, 2017.

[50] Matheus Gadelha, Subhransu Maji, and Rui Wang, 3d shape induction from 2d views of multiple objects. arXiv preprint arXiv:1612.05872, 2016.

[51] X.L.Wang, A.Shrivastava, and A.Gupta, A-Fast-RCNN: hardpositive generation via adversary for object detection, arXiv: 1704.03414, 2017.

[52] Chongxuan Li, Kun Xu, Jun Zhu, and Bo Zhang, Triple generative adversarial nets.arXiv preprint arXiv:1703.02291, 2017.

[53] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 22342242, 2016.

[54] Martin Arjovsky and Leon Bottou, Towards principled methods for training generative adversarial networks. In International Conference on Learning Representations, 2017. Under review.

[55] Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In Advances in Neural Information Processing Systems, pages 8290, 2016.

[56] Sagie Benaim and Lior Wolf, One-sided unsupervised domain mapping,arXiv preprint arXiv:1706.00826, 2017.

[57] White, T. Sampling Generative Networks, 2016; http://arxiv.org/abs/1609.04468.

[58] Huiskes,M.J.andLew,M.S.(2008).Themirflickrretrievalevaluation. In MIR 08: Proceedings of the 2008 ACM International Conference on Multimedia Information Retrieval, New York, NY, USA. ACM.

[59] Ian J. Goodfellow, Jonathon Shlens and Christian Szegedy, EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES, arXiv:1412.6572v3, 2015.

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How to Cite

Bhandarkar, T., & Murugan, A. (2018). Understanding Trending Variants of Generative Adversarial Networks. International Journal of Engineering & Technology, 7(3.12), 864–870. https://doi.org/10.14419/ijet.v7i3.12.16552
Received 2018-07-30
Accepted 2018-07-30
Published 2018-07-20