Abstract
Deep Learning models can achieve impressive performance in supervised learning but not for unsupervised one. In image generation problem for example, we have no concrete target vector. Generative models have been proven useful for solving this kind of issues. In this paper, we will compare two types of generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We apply those methods to different data sets to point out their differences and see their capabilities and limits as well. We find that, while VAEs are easier and faster to train, their results are in general more blurry than the images generated by GANs. These last are more realistic but noisy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goyal, P., Hu, Z., Liang, X., Wang, C., Xing, E.: Nonparametric variational autoencoders for hierarchical representation learning. arXiv:1703.07027 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv:1312.6114 (2013)
Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: ICML, pp. 1278–1286 (2014)
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: a recurrent neural network for image generation. arXiv preprint arXiv:1502.04623 (2015)
Bordes, A., Bottou, L., Gallinari, P.: SGD-QN: careful quasi-Newton stochastic gradient descent. J. Mach. Learn. Res. 10, 1737–1754 (2009)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV), December 2015
Gu, X., Knutsson, H., Nilsson, M., Eklund, A.: Generating diffusion MRI scalar maps from T1 weighted images using generative adversarial networks. CoRR (2018)
Abbasnejad, M.E., Dick, A., van den Hengel, A.: 2017 IEEE Conference on Computer Vision a (2017)
Yeh, R.A., Chen, C., Lim, T.Y., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5485–5493 (2017)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network (2016)
Zhao, F., Feng, J., Zhao, J., Yang, W., Yan, S.: Robust lstmautoencoders for face de-occlusion in the wild. IEEE Trans. Image Process. 27(2), 778–790 (2018)
Press, O., Bar, A., Bogin, B., Berant, J., Wolf, L.: Language generation with recurrent generative adversarial networks without pre-training. arXiv preprint arXiv:1706.01399 (2017)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: AAAI 2017, pp. 2852–2858 (2017)
Barratt, S., Sharma, R.: A note on the inception score. In: ICML Workshop (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
El-Kaddoury, M., Mahmoudi, A., Himmi, M.M. (2019). Deep Generative Models for Image Generation: A Practical Comparison Between Variational Autoencoders and Generative Adversarial Networks. In: Renault, É., Boumerdassi, S., Leghris, C., Bouzefrane, S. (eds) Mobile, Secure, and Programmable Networking. MSPN 2019. Lecture Notes in Computer Science(), vol 11557. Springer, Cham. https://doi.org/10.1007/978-3-030-22885-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-22885-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22884-2
Online ISBN: 978-3-030-22885-9
eBook Packages: Computer ScienceComputer Science (R0)