Abstract
Generative adversarial networks (GANs) introduced by Goodfellow et al. since their advent have had a number of improvements and applications in image generation tasks and unsupervised learning. Recurrent model and the conditional models are two derivations of GANs. In this paper, conditional recurrent GAN is proposed. By using conditional settings in recurrent GANs, they can be used to generate state-of-the-art images. The conditional and recurrent models are compared with the proposed model using the generative adversarial metric proposed by Im et al. where the discriminator of one model competes against the generator of another. The results show that the proposed model outperforms the other two models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Yann, Bengio, Yoshua, Hinton, Geoffrey: Deep learning. Nature 521(7553), 436–444 (2015)
Hinton, G.E.: Deep belief networks. Scholarpedia 4(5), 5947 (2009)
LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. In: Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science, vol 1681. Springer, Berlin, Heidelberg (1999)
Im, D.J., Kim, C.D., Jiang, H., Memisevic R.: Generating images with recurrent adversarial networks. (2016). arXiv:1602.05110
Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR, pp. 1538–1546 (2015)
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra D.: Draw: a recurrent neural network for image generation. In: Proceedings of the International Conference on Machine Learning (ICML) (2015)
Radford, A., Metz, L., Chintala. S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)
Denton, E.L., Chintala, S., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486−1494 (2015)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2234−2242 (2016)
Ranzato, M., Mnih, V., Susskind, J.M., Hinton, G.E.: Modeling natural images using gated mrfs. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2206–2222 (2013)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 248−255. IEEE (2009)
Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. (2015). arXiv:1508.06576
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proceedings of the Neural Information Processing Systems (NIPS) (2013)
Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli S.: Deep unsupervised learning using nonequilibrium thermodynamics In: Proceedings of The 32nd International Conference on Machine Learning (2015)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio Y.: Generative Adversarial Networks. In: Advances in Neural Information Processing Systems, pp. 2672− 680 (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. In: Proceedings of the Neural Information Processing Systems Deep learning Workshop (NIPS) (2014)
Li, C., Wand, M.: Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European Conference on Computer Vision, pp. 702−716. Springer International Publishing (2016)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30 (2013)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Seth, S., Zaveri, M.A. (2019). Conditional Generative Recurrent Adversarial Networks. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_42
Download citation
DOI: https://doi.org/10.1007/978-981-13-1921-1_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1920-4
Online ISBN: 978-981-13-1921-1
eBook Packages: EngineeringEngineering (R0)