Abstract
Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.
- Pieter Abbeel and Andrew Y. Ng. 2011. Inverse reinforcement learning. In Encyclopedia of Machine Learning. Springer, 554--558.Google Scholar
- Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, and Mario Marchand. 2014. Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014).Google Scholar
- Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay. 2017. Face aging with conditional generative adversarial networks. arXiv preprint arXiv:1702.01983 (2017).Google Scholar
- Martin Arjovsky and Léon Bottou. 2017. Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017).Google Scholar
- Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017).Google Scholar
- Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. 2017. Generalization and equilibrium in generative adversarial nets (GANs). arXiv preprint arXiv:1703.00573 (2017). Google ScholarDigital Library
- Shane Barratt and Rishi Sharma. 2018. A note on the inception score. arXiv preprint arXiv:1801.01973 (2018).Google Scholar
- Marc G, Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos. 2017. The cramer distance as a solution to biased Wasserstein gradients. arXiv preprint arXiv:1705.10743 (2017).Google Scholar
- Sagie Benaim and Lior Wolf. 2017. One-sided unsupervised domain mapping. arXiv preprint arXiv:1706.00826 (2017). Google ScholarDigital Library
- David Berthelot, Tom Schumm, and Luke Metz. 2017. BeGAN: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017).Google Scholar
- Ashish Bora, Eric Price, and Alexandros G. Dimakis. 2018. AmbientGAN: Generative models from lossy measurements. In Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=Hy7fDog0b.Google Scholar
- Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan. 2016. Unsupervised pixel-level domain adaptation with generative adversarial networks. arXiv preprint arXiv:1612.05424 (2016).Google Scholar
- Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li. 2016. Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136 (2016).Google Scholar
- Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio. 2017. Maximum-likelihood augmented discrete generative adversarial networks. arXiv preprint arXiv:1702.07983 (2017).Google Scholar
- Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, 2172--2180. Google ScholarDigital Library
- Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, and Eric P. Xing. 2017. SCAN: Structure correcting adversarial network for chest x-rays organ segmentation. arXiv preprint arXiv:1703.08770 (2017).Google Scholar
- Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, and Peter Dayan. 2017. Comparison of maximum likelihood and GAN-based training of real NVPs. arXiv preprint arXiv:1705.05263 (2017).Google Scholar
- Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed, Muhammad Zeshan Afzal, and Marcus Liwicki. 2017. TAC-GAN-text conditioned auxiliary classifier generative adversarial network. arXiv preprint arXiv:1703.06412 (2017).Google Scholar
- Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng. 2018. Training GANs with optimism. In International Conference on Learning Representations. https://openreview.net/forum?id=SJJySbbAZ.Google Scholar
- Roy De Maesschalck, Delphine Jouan-Rimbaud, and Désiré L. Massart. 2000. The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems 50, 1 (2000), 1--18.Google ScholarCross Ref
- Emily Denton, Sam Gross, and Rob Fergus. 2016. Semi-supervised learning with context-conditional generative adversarial networks. arXiv preprint arXiv:1611.06430 (2016).Google Scholar
- Carl Doersch. 2016. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016).Google Scholar
- Chris Donahue, Akshay Balsubramani, Julian McAuley, and Zachary C. Lipton. 2017. Semantically decomposing the latent spaces of generative adversarial networks. arXiv preprint arXiv:1705.07904 (2017).Google Scholar
- Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. 2016. Adversarial feature learning. arXiv preprint arXiv:1605.09782 (2016).Google Scholar
- Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville. 2016. Adversarially learned inference. arXiv preprint arXiv:1606.00704 (2016).Google Scholar
- Vincent Dumoulin and Francesco Visin. 2016. A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016).Google Scholar
- Ishan Durugkar, Ian Gemp, and Sridhar Mahadevan. 2016. Generative multi-adversarial networks. arXiv preprint arXiv:1611.01673 (2016).Google Scholar
- Kiana Ehsani, Roozbeh Mottaghi, and Ali Farhadi. 2017. SeGAN: Segmenting and generating the invisible. arXiv preprint arXiv:1703.10239 (2017).Google Scholar
- Werner Fenchel. 1949. On conjugate convex functions. Canadian Journal of Mathematics 1, 73--77 (1949).Google ScholarCross Ref
- Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine. 2016. A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models. arXiv preprint arXiv:1611.03852 (2016).Google Scholar
- Robert T. Frankot and Rama Chellappa. 1988. A method for enforcing integrability in shape from shading algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 4 (1988), 439--451. Google ScholarDigital Library
- Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE, 2414--2423.Google ScholarCross Ref
- Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, and Puneet K. Dokania. 2017. Multi-agent diverse generative adversarial networks. arXiv preprint arXiv:1704.02906 (2017).Google Scholar
- Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, and Lukasz Kaiser. 2018. Unsupervised cipher cracking using discrete GANs. In Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=BkeqO7x0-.Google Scholar
- Ian Goodfellow. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016).Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, 2672--2680. Google ScholarDigital Library
- Mahesh Gorijala and Ambedkar Dukkipati. 2017. Image generation and editing with variational info generative AdversarialNetworks. arXiv preprint arXiv:1701.04568 (2017).Google Scholar
- Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’13). IEEE, 6645--6649.Google ScholarCross Ref
- Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause. 2017. An online learning approach to generative adversarial networks. arXiv preprint arXiv:1706.03269 (2017).Google Scholar
- Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, and Sergey Levine. 2016. Q-prop: Sample-efficient policy gradient with an off-policy critic. arXiv preprint arXiv:1611.02247 (2016).Google Scholar
- Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik. 2017. Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv preprint arXiv:1705.10843 (2017).Google Scholar
- Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017. Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017). Google ScholarDigital Library
- Leonid G. Hanin. 1992. Kantorovich-rubinstein norm and its application in the theory of Lipschitz spaces. Proc. Amer. Math. Soc. 115, 2 (1992), 345--352.Google ScholarCross Ref
- R. Devon Hjelm, Athul Paul Jacob, Tong Che, Kyunghyun Cho, and Yoshua Bengio. 2017. Boundary-seeking generative adversarial networks. arXiv preprint arXiv:1702.08431 (2017).Google Scholar
- Jonathan Ho and Stefano Ermon. 2016. Generative adversarial imitation learning. In Advances in Neural Information Processing Systems. Curran Associates, 4565--4573. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, and Trevor Darrell. 2017. CyCADA: Cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213 (2017).Google Scholar
- Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, and Hsin-Min Wang. 2017. Voice conversion from unaligned corpora using variational autoencoding Wasserstein generative adversarial networks. arXiv preprint arXiv:1704.00849 (2017).Google Scholar
- Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie. 2016. Stacked generative adversarial networks. arXiv:1612.04357 (2016). https://arxiv.org/abs/1612.04357.Google Scholar
- Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie. 2017. Stacked generative adversarial networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2. IEEE, 4.Google ScholarCross Ref
- Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. 2016. Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110 (2016).Google Scholar
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2016. Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016).Google Scholar
- Edwin T. Jaynes. 1957. Information theory and statistical mechanics. Physical Review 106, 4 (1957), 620.Google ScholarCross Ref
- Felix Juefei-Xu, Vishnu Naresh Boddeti, and Marios Savvides. 2017. Gang of GANs: Generative adversarial networks with maximum margin ranking. arXiv preprint arXiv:1704.04865 (2017).Google Scholar
- Levent Karacan, Zeynep Akata, Aykut Erdem, and Erkut Erdem. 2016. Learning to generate images of outdoor scenes from attributes and semantic layouts. arXiv preprint arXiv:1612.00215 (2016).Google Scholar
- Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017).Google Scholar
- Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jungkwon Lee, and Jiwon Kim. 2017. Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017). Google ScholarDigital Library
- Taeksoo Kim, Byoungjip Kim, Moonsu Cha, and Jiwon Kim. 2017. Unsupervised visual attribute transfer with reconfigurable generative adversarial networks. arXiv preprint arXiv:1707.09798 (2017).Google Scholar
- Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun, et al. 2017. Adversarially regularized autoencoders for generating discrete structures. arXiv preprint arXiv:1706.04223 (2017).Google Scholar
- Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, and Sriram Vishwanath. 2017. CausalGAN: Learning causal implicit generative models with adversarial training. arXiv preprint arXiv:1709.02023 (2017).Google Scholar
- Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. 2017. How to train your DRAGAN. arXiv preprint arXiv:1705.07215 (2017).Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. Curran Associates, 1097--1105. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097--1105. Google ScholarDigital Library
- Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2015. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015).Google Scholar
- Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 (2016).Google Scholar
- Sang-gil Lee, Uiwon Hwang, Seonwoo Min, and Sungroh Yoon. 2017. A SeqGAN for polyphonic music generation. arXiv preprint arXiv:1710.11418 (2017).Google Scholar
- Chongxuan Li, Kun Xu, Jun Zhu, and Bo Zhang. 2017. Triple generative adversarial nets. arXiv preprint arXiv:1703.02291 (2017).Google Scholar
- Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos. 2017. MMD GAN: Towards deeper understanding of moment matching network. arXiv preprint arXiv:1705.08584 (2017). Google ScholarDigital Library
- Jianan Li, Xiaodan Liang, Yunchao Wei, Tingfa Xu, Jiashi Feng, and Shuicheng Yan. 2017. Perceptual generative adversarial networks for small object detection. In IEEE CVPR.Google Scholar
- Yujia Li, Kevin Swersky, and Rich Zemel. 2015. Generative moment matching networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML’15). 1718--1727. Google ScholarDigital Library
- Timothy P, Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015).Google Scholar
- Jae Hyun Lim and Jong Chul Ye. 2017. Geometric GAN. arXiv preprint arXiv:1705.02894 (2017).Google Scholar
- Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. 2017. Adversarial ranking for language generation. arXiv preprint arXiv:1705.11001 (2017). Google ScholarDigital Library
- Ming-Yu Liu and Oncel Tuzel. 2016. Coupled generative adversarial networks. In Advances in Neural Information Processing Systems. Curran Associates, 469--477. Google ScholarDigital Library
- Yongyi Lu, Yu-Wing Tai, and Chi-Keung Tang. 2017. Conditional CycleGAN for attribute guided face image generation. arXiv preprint arXiv:1705.09966 (2017).Google Scholar
- Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, and Stephen Paul Smolley. 2016. Least squares generative adversarial networks. arXiv preprint ArXiv:1611.04076 (2016).Google Scholar
- Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M. Pauly, et al. 2017. Deep generative adversarial networks for compressed sensing automates MRI. arXiv preprint arXiv:1706.00051 (2017).Google Scholar
- Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. The numerics of GANs. arXiv preprint arXiv:1705.10461 (2017).Google Scholar
- Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2016. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016).Google Scholar
- Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).Google Scholar
- Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018).Google Scholar
- Takeru Miyato and Masanori Koyama. 2018. cGANs with projection discriminator. arXiv preprint arXiv:1802.05637 (2018).Google Scholar
- Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904 (2016).Google Scholar
- Youssef Mroueh and Tom Sercu. 2017. Fisher GAN. arXiv preprint arXiv:1705.09675 (2017). Google ScholarDigital Library
- Youssef Mroueh, Tom Sercu, and Vaibhava Goel. 2017. McGan: Mean and covariance feature matching GAN. arXiv preprint arXiv:1702.08398 (2017). Google ScholarDigital Library
- Hariharan Narayanan and Sanjoy Mitter. 2010. Sample complexity of testing the manifold hypothesis. In Advances in Neural Information Processing Systems. Curran Associates, 1786--1794. Google ScholarDigital Library
- Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In Advances in Neural Information Processing Systems. Curran Associates, 3387--3395. Google ScholarDigital Library
- Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune. 2016. Plug 8 play generative networks: Conditional iterative generation of images in latent space. arXiv preprint arXiv:1612.00005 (2016).Google Scholar
- Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-GAN: Training generative neural samplers using variational divergence minimization. In Advances in Neural Information Processing Systems. Curran Associates, 271--279. Google ScholarDigital Library
- Augustus Odena, Christopher Olah, and Jonathon Shlens. 2016. Conditional image synthesis with auxiliary classifier GANs. arXiv preprint arXiv:1610.09585 (2016). Google ScholarDigital Library
- Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016).Google Scholar
- Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016).Google Scholar
- Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345--1359. Google ScholarDigital Library
- Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, and Jose M. Álvarez. 2016. Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355 (2016).Google Scholar
- Patrick Pérez, Michel Gangnet, and Andrew Blake. 2003. Poisson image editing. In ACM Transactions on Graphics (TOG), Vol. 22. ACM, 313--318. Google ScholarDigital Library
- Dmitry Pestov, Xi Wang, Gombojav O. Ariunbold, Robert K. Murawski, Vladimir A. Sautenkov, Arthur Dogariu, Alexei V. Sokolov, and Marlan O. Scully. 2008. Single-shot detection of bacterial endospores via coherent Raman spectroscopy. Proceedings of the National Academy of Sciences 105, 2 (2008), 422--427.Google ScholarCross Ref
- David Pfau and Oriol Vinyals. 2016. Connecting generative adversarial networks and actor-critic methods. arXiv preprint arXiv:1610.01945 (2016).Google Scholar
- Guo-Jun Qi. 2017. Loss-sensitive generative adversarial networks on Lipschitz densities. arXiv preprint arXiv:1701.06264 (2017).Google Scholar
- Svetlozar Todorov Rachev et al. 1990. Duality theorems for Kantorovich-Rubinstein and Wasserstein functionals. (1990).Google Scholar
- Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779--788.Google ScholarCross Ref
- Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, and Shakir Mohamed. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv preprint arXiv:1706.04987 (2017).Google Scholar
- Murray Rosenblatt. 1956. A central limit theorem and a strong mixing condition. Proceedings of the National Academy of Sciences 42, 1 (1956), 43--47.Google ScholarCross Ref
- Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Advances in Neural Information Processing Systems. Curran Associates, 2234--2242. Google ScholarDigital Library
- Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. 2017. Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517 (2017).Google Scholar
- Bernhard Schölkopf and Alexander J. Smola. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press.Google Scholar
- John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. 2015. Trust region policy optimization. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 1889--1897. Google ScholarDigital Library
- Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu. 2017. Adversarial representation learning for domain adaptation. arXiv preprint arXiv:1707.01217 (2017).Google Scholar
- Haichao Shi, Jing Dong, Wei Wang, Yinlong Qian, and Xiaoyu Zhang. 2017. SSGAN: Secure steganography based on generative adversarial networks. arXiv preprint arXiv:1707.01613 (2017).Google Scholar
- Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. 2017. Continual learning with deep generative replay. In Advances in Neural Information Processing Systems. Curran Associates, 2990--2999. Google ScholarDigital Library
- Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon. 2018. A DIRT-T approach to unsupervised domain adaptation. arXiv preprint arXiv:1802.08735 (2018).Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Paul Smolensky. 1986. Information Processing in Dynamical Systems: Foundations of Harmony Theory. Technical Report. Colorado University at Boulder, Department of Computer Science.Google Scholar
- Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, Wangmeng Zuo, Chunhua Shen, Rynson Lau, and Ming-Hsuan Yang. 2018. VITAL: VIsual tracking via adversarial learning. arXiv preprint arXiv:1804.04273 (2018).Google Scholar
- Jost Tobias Springenberg. 2015. Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390 (2015).Google Scholar
- Adrian Spurr, Emre Aksan, and Otmar Hilliges. 2017. Guiding InfoGAN with semi-supervision. arXiv preprint arXiv:1707.04487 (2017).Google Scholar
- Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert RG Lanckriet. 2009. On integral probability metrics,phi-divergences and binary classification. arXiv preprint arXiv:0901.2698 (2009).Google Scholar
- Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems. Curran Associates, 1057--1063. Google ScholarDigital Library
- Lucas Theis, Aäron van den Oord, and Matthias Bethge. 2015. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 (2015).Google Scholar
- Alberto Torchinsky and Shilin Wang. 1990. A note on the Marcinkiewicz integral. In Colloquium Mathematicae, Vol. 1. 235--243.Google ScholarCross Ref
- Luan Tran, Xi Yin, and Xiaoming Liu. 2017. Representation learning by rotating your faces. arXiv preprint arXiv:1705.11136 (2017).Google Scholar
- Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. 2017. MocoGAN: Decomposing motion and content for video generation. arXiv preprint arXiv:1707.04993 (2017).Google Scholar
- Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2017. Adversarial generator-encoder networks. arXiv preprint arXiv:1704.02304 (2017).Google Scholar
- Denis Volkhonskiy, Ivan Nazarov, Boris Borisenko, and Evgeny Burnaev. 2017. Steganographic generative adversarial networks. arXiv preprint arXiv:1703.05502 (2017).Google Scholar
- Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. 2016. Generating videos with scene dynamics. In Advances In Neural Information Processing Systems. 613--621. Google ScholarDigital Library
- Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert. 2017. The pose knows: Video forecasting by generating pose futures. arXiv preprint arXiv:1705.00053 (2017).Google Scholar
- Chaoyue Wang, Chang Xu, Chaohui Wang, and Dacheng Tao. 2018. Perceptual adversarial networks for image-to-image transformation. IEEE Transactions on Image Processing 27, 8 (2018), 4066--4079.Google ScholarCross Ref
- Ruohan Wang, Antoine Cully, Hyung Jin Chang, and Yiannis Demiris. 2017. MAGAN: Margin adaptation for generative adversarial networks. arXiv preprint arXiv:1704.03817 (2017).Google Scholar
- Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600--612. Google ScholarDigital Library
- Max Welling. 2005. Fisher linear discriminant analysis.Department of Computer Science, University of Toronto, 3, 1 (2005).Google Scholar
- Edwin B. Wilson and Margaret M. Hilferty. 1931. The distribution of chi-square. Proceedings of the National Academy of Sciences 17, 12 (1931), 684--688.Google ScholarCross Ref
- Huikai Wu, Shuai Zheng, Junge Zhang, and Kaiqi Huang. 2017. GP-GAN: Towards realistic high-resolution image blending. arXiv preprint arXiv:1703.07195 (2017).Google Scholar
- Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In Advances in Neural Information Processing Systems. Curran Associates, 82--90. Google ScholarDigital Library
- Yuan Xue, Tao Xu, Han Zhang, Rodney Long, and Xiaolei Huang. 2017. SegAN: Adversarial network with multi-scale loss for medical image segmentation. arXiv preprint arXiv:1706.01805 (2017).Google Scholar
- Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2016. Attribute2image: Conditional image generation from visual attributes. In Proceedings of the European Conference on Computer Vision. Springer, 776--791.Google ScholarCross Ref
- Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S. Kevin Zhou, Zhoubing Xu, JinHyeong Park, Mingqing Chen, Trac D. Tran, et al. 2017. Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization. In Proceedings of the International Conference on Information Processing in Medical Imaging. Springer, 633--644.Google ScholarCross Ref
- Zili Yi, Hao Zhang, Ping Tan Gong, et al. 2017. DualGAN: Unsupervised dual learning for image-to-image translation. arXiv preprint arXiv:1704.02510 (2017).Google Scholar
- Weidong Yin, Yanwei Fu, Leonid Sigal, and Xiangyang Xue. 2017. Semi-latent GAN: Learning to generate and modify facial images from attributes. arXiv preprint arXiv:1704.02166 (2017).Google Scholar
- Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, and Sungroh Yoon. 2017. Energy-based sequence GANs for recommendation and their connection to imitation learning. arXiv preprint arXiv:1706.09200 (2017).Google Scholar
- Jaeyoon Yoo, Yongjun Hong, and Sungroh Yoon. 2017. Autonomous UAV navigation with domain adaptation. arXiv preprint arXiv:1712.03742 (2017).Google Scholar
- Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. SeqGAN: Sequence generative adversarial nets with policy gradient. In AAAI. 2852--2858. Google ScholarDigital Library
- Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas. 2016. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv preprint arXiv:1612.03242 (2016).Google Scholar
- Junbo Zhao, Michael Mathieu, and Yann LeCun. 2016. Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126 (2016).Google Scholar
- Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, and Weiran He. 2017. GeneGAN: Learning object transfiguration and attribute subspace from unpaired data. arXiv preprint arXiv:1705.04932 (2017).Google Scholar
- Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017).Google Scholar
- Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, and Anind K. Dey. 2008. Maximum entropy inverse reinforcement learning. In AAAI, Vol. 8. Chicago, IL, 1433--1438. Google ScholarDigital Library
Index Terms
- How Generative Adversarial Networks and Their Variants Work: An Overview
Recommendations
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major ...
Text to image synthesis using multi-generator text conditioned generative adversarial networks
AbstractRecently, Generative Adversarial Network(GAN) has been the most mainstream technology in the task of Text to Image. However, the vanilla deep neural networks tend to approximate continuous mappings in real generation tasks rather than ...
CapsuleGAN: Generative Adversarial Capsule Network
Computer Vision – ECCV 2018 WorkshopsAbstractWe present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) ...
Comments