P. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, Context encoders: Feature learning by inpainting, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2536-2544, 2016.

R. Zhang, P. Isola, and A. A. Efros, Colorful image colorization, pp.649-666, 2016.

B. O. Ayinde and J. M. Zurada, Deep learning of constrained autoencoders for enhanced understanding of data, IEEE Transactions on Neural Networks and Learning Systems, vol.29, issue.9, pp.3969-3979, 2018.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Improved techniques for training gans, Advances in Neural Information Processing Systems, pp.2234-2242, 2016.

K. Kurach, M. Lucic, X. Zhai, M. Michalski, and S. Gelly, The gan landscape: Losses, architectures, regularization, and normalization, 2018.

S. Nowozin, B. Cseke, and R. Tomioka, f-gan: Training generative neural samplers using variational divergence minimization, Advances in Neural Information Processing Systems, pp.271-279, 2016.

M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, International Conference on Machine Learning, pp.214-223, 2017.

X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang et al., Least squares generative adversarial networks, 2017 IEEE International Conference on, pp.2813-2821, 2017.

M. Arjovsky and L. Bottou, Towards principled methods for training generative adversarial networks, 2017.

M. Mirza and S. Osindero, Conditional generative adversarial nets, 2014.

J. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, 2017 IEEE International Conference on, pp.2242-2251, 2017.

P. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5967-5976, 2017.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele et al., Generative adversarial text to image synthesis, 33rd International Conference on Machine Learning, pp.1060-1069, 2016.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham et al., Photo-realistic single image super-resolution using a generative adversarial network, Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference, pp.105-114, 2017.

S. Azadi, M. Fisher, V. Kim, Z. Wang, E. Shechtman et al., Multi-content gan for few-shot font style transfer

K. Grewal, R. D. Hjelm, and Y. Bengio, Variance regularizing adversarial learning, 2017.

L. Metz, B. Poole, D. Pfau, and J. Sohl-dickstein, Unrolled generative adversarial networks, 2017.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, Improved training of wasserstein gans, Advances in Neural Information Processing Systems, pp.5767-5777, 2017.

K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann, Stabilizing training of generative adversarial networks through regularization, Advances in Neural Information Processing Systems, pp.2018-2028, 2017.

T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li, Mode regularized generative adversarial networks, 2017.

T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, Spectral normalization for generative adversarial networks, 2018.

A. Brock, T. Lim, J. M. Ritchie, and N. Weston, Neural photo editing with introspective adversarial networks, 2016.

C. Liu, Z. Zhang, and D. Wang, Pruning deep neural networks by optimal brain damage, Fifteenth Annual Conference of the International Speech Communication Association, 2014.

P. Xie, Y. Deng, and E. Xing, On the generalization error bounds of neural networks under diversity-inducing mutual angular regularization, 2015.

P. Rodríguez, J. Gonzàlez, G. Cucurull, J. M. Gonfaus, and X. Roca, Regularizing cnns with locally constrained decorrelations, 2017.

A. Dundar, J. Jin, and E. Culurciello, Convolutional clustering for unsupervised learning, 2015.

B. O. Ayinde and J. M. Zurada, Building efficient convnets using redundant feature pruning, 2018.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, pp.1097-1105, 2012.

M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, European Conference on Computer Vision, pp.818-833, 2014.

B. O. Ayinde and J. M. Zurada, Clustering of receptive fields in autoencoders, Neural Networks (IJCNN), 2016 International Joint Conference on, pp.1310-1317, 2016.

B. O. Ayinde, T. Inanc, and J. M. Zurada, Regularizing deep neural networks by enhancing diversity in feature extraction, IEEE Transactions on Neural Networks and Learning Systems, pp.1-12, 2019.

A. M. Saxe, J. L. Mcclelland, and S. Ganguli, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2014.

B. O. Ayinde and J. M. Zurada, Nonredundant sparse feature extraction using autoencoders with receptive fields clustering, Neural Networks, vol.93, pp.99-109, 2017.

Y. Lecun, The mnist database of handwritten digits, 1998.

A. Krizhevsky and G. Hinton, Learning multiple layers of features from tiny images, 2009.

A. Coates, A. Ng, and H. Lee, An analysis of single-layer networks in unsupervised feature learning, Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp.215-223, 2011.

Z. Liu, P. Luo, X. Wang, and X. Tang, Deep learning face attributes in the wild, Proceedings of the IEEE International Conference on Computer Vision, pp.3730-3738, 2015.

D. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

S. Vallender, Calculation of the wasserstein distance between probability distributions on the line, Theory of Probability & Its Applications, vol.18, pp.784-786, 1974.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

J. L. Ba, J. R. Kiros, and G. E. Hinton, Layer normalization, 2016.

T. Salimans and D. P. Kingma, Weight normalization: A simple reparameterization to accelerate training of deep neural networks, Advances in Neural Information Processing Systems, pp.901-909, 2016.