References

Arjovsky, M., S. Chintala and L. Bottou, 2017. Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, July 7-10, 2017, London, UK., pp: 214-223.

Ba, J.L., J.R. Kiros and G.E. Hinton, 2016. Layer normalization. Mach. Learn., 1: 1-14.
Direct Link  |  

Chen, B.C., Y.Y. Chen, Y.H. Kuo and W.H. Hsu, 2013. Scalable face image retrieval using attribute-enhanced sparse codewords. IEEE. Trans. Multimedia, 15: 1163-1173.
CrossRef  |  Direct Link  |  

Dai, Z., A. Almahairi, P. Bachman, E. Hovy and A. Courville, 2017. Calibrating energy-based generative adversarial networks. Proceedings of the ICLR 2017 Track International Conference on Learning Representations, April 24-26, 2017, Toulon, France, pp: 1-17.

Denton, E.L., S. Chintala and R. Fergus, 2015. Deep Generative Image Models Using a  Laplacian Pyramid of Adversarial Networks. In: Advances in Neural Information Processing Systems, Cortes, C., N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett (Eds.). Curran Associates Inc., New York, USA., pp: 1486-1494.

Dumoulin, V., I. Belghazi, B. Poole, O. Mastropietro and A. Lamb et al., 2016. Adversarially learned inference. Mach. Learn., 1: 1-18.
Direct Link  |  

Durugkar, I., I. Gemp and S. Mahadevan, 2016. Generative multi-adversarial networks. Mach. Learn., 1: 1-14.
Direct Link  |  

Ghosh, A., V. Kulharia, V.P. Namboodiri, P.H. Torr and P.K. Dokania, 2018. Multi-agent diverse generative adversarial networks. Proceedings of the 2018 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2018), June 18-22, 2018, CVPR, Salt Lake, Utah, pp: 8513-8521.

Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu and D. Warde-Farley et al., 2014. Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, December 08-13, 2014, ACM, Montreal, Canada, pp: 2672-2680.

Gregor, K., I. Danihelka, A. Graves, D.J. Rezende and D. Wierstra, 2015. Draw: A recurrent neural network for image generation. Comput. Vision Pattern Recognit., 1: 1-10.
Direct Link  |  

Grinblat, G.L., L.C. Uzal and P.M. Granitto, 2017. Class-splitting generative adversarial networks. Mach. Learn., 1: 1-10.
Direct Link  |  

Gulrajani, I., F. Ahmed, M. Arjovsky, V. Dumoulin and A.C. Courville, 2017. Improved Training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, Guyon, I., U.V. Luxburg, S. Bengio, H. Wallach and R. Fergus et al. (Eds.). Curran Associates Inc., New York, USA., pp: 5767-5777.

He, K., X. Zhang, S. Ren and J. Sun, 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE. Trans. Pattern Anal. Mach. Intell., 37: 1904-1916.
CrossRef  |  PubMed  |  Direct Link  |  

Heusel, M., H. Ramsauer, T. Unterthiner, B. Nessler and S. Hochreiter, 2017. Gans trained by a two time-scale update rule converge to a local NASH equilibrium. Proceedings of the 2017 31st Annual International Conference on Neural Information Processing Systems (NIPS 2017), December 4-9, 2017, Long Beach, California, USA., pp: 1-38.

Huang, X., Y. Li, O. Poursaeed, J. Hopcroft and S. Belongie, 2017. Stacked generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, Hawaii, USA., ISBN:978-1-5386-0458-8, pp: 1866-1875.

Ioffe, S. and C. Szegedy, 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, July 07-09, 2015, Microtome Publishing, Lille, France, pp: 448-456.

Karras, T., T. Aila, S. Laine and J. Lehtinen, 2017. Progressive growing of GANs for improved quality, stability and variation. Neural Evol. Comput., 1: 1-26.
Direct Link  |  

Khosla, A., W.A. Bainbridge, A. Torralba and A. Oliva, 2013. Modifying the memorability of face photographs. Proceedings of the IEEE International Conference on Computer Vision (ICCV '13), December 1-8, 2013, IEEE, Washington, DC., USA., ISBN:978-1-4799-2840-8, pp: 3200-3207.

Kingma, D.P. and M. Welling, 2013. Auto-encoding variational bayes. Mach. Learn., 1: 1-14.
Direct Link  |  

Nair, V. and G.E. Hinton, 2010. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, ACM, Haifa, Israel, ISBN:978-1-60558-907-7, pp: 807-814.

Ngiam, J., A. Khosla, M. Kim, J. Nam and H. Lee et al., 2011. Multimodal deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML-11), June 28-July 2, 2011, Bellevue, Washington, USA., pp: 689-696.

Odena, A., C. Olah and J. Shlens, 2017. Conditional image synthesis with auxiliary classifier GANs. Proceedings of the 34th International Conference on Machine Learning (ICML'17) Vol. 70, August 6-11, 2017, JMLR.Org, Sydney, Australia, pp: 2642-2651.

Radford, A., L. Metz and S. Chintala, 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. Mach. Learn., 1: 1-16.
Direct Link  |  

Reed, S., Z. Akata, X. Yan, L. Logeswaran and B. Schiele et al., 2016. Generative adversarial text to image synthesis. Neural Evol. Comput., 1: 1-10.
Direct Link  |  

Salimans, T. and D.P. Kingma, 2016. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. In: Advances in Neural Information Processing Systems, Lee, D.D., M. Sugiyama, U.V. Luxburg, I. Guyon and R. Garnett (Eds.). Curran Associates Inc., New York, USA., pp: 901-909.

Simonyan, K. and A. Zisserman, 2014. Very deep convolutional networks for large-scale image recognition. Master Thesis, Cornell University, Ithaca, New York.

Sohl-Dickstein, J., E.A. Weiss, N. Maheswaranathan and S. Ganguli, 2015. Deep unsupervised learning using nonequilibrium thermodynamics. Mach. Learn., 1: 1-18.
Direct Link  |  

Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna and D. Erhan et al., 2013. Intriguing properties of neural networks. Comput. Vision Pattern Recognit., 1: 1-10.
Direct Link  |  

Theis, L., A.V.D. Oord and M. Bethge, 2015. A note on the evaluation of generative models. Mach. Learn., 1: 1-10.
Direct Link  |  

Tu, Z., 2007. Learning generative models via discriminative approaches. Proceedings of the 2007 International IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, 2007, IEEE, Minneapolis, Minnesota, USA., pp: 1-8.

Yang, J., A. Kannan, D. Batra and D. Parikh, 2017. LR-GAN: Layered recursive generative adversarial networks for image generation. Comput. Vision Pattern Recognit., 1: 1-21.
Direct Link  |  

Yu, F., A. Seff, Y. Zhang, S. Song and T. Funkhouser et al., 2015. LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop. Comput. Vision Pattern Recognit., 1: 1-9.
Direct Link  |  

Zeiler, M.D. and R. Fergus, 2014. Visualizing and Understanding Convolutional Networks. In: Computer Vision, Fleet, D., T. Pajdla, B. Schiele and T. Tuytelaars (Eds.). Springer, Cham, Switzerland, ISBN:978-3-319-10589-5, pp: 818.