Abstract
The inverse mapping of GANs’ (Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator (IG) model as encoder and pre-trained generator (G) as decoder of an AutoEncoder network to train the IG model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN’s generator, of AutoEncoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function. We also applied the inverse model of GANs’ generators to image searching and translation. The experimental results prove that the proposed approach works better than the traditional approaches in image searching.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goodfellow, I.J., Pougetabadie, J., Mirza, M., Xu, B., Wardefarley, D., Ozair, S., Courville, A., Bengio, Y., Ghahramani, Z., Welling, M.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 3, pp. 2672–2680 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv preprint: arXiv:1511.06434
Dong, H., Neekhara, P., Wu, C., Guo, Y.: Unsupervised image-to-image translation with generative adversarial networks (2017). arXiv preprint: arXiv:1701.02676
Perarnau, G., van de Weijer, J., Raducanu, B., Alvarez, J.M.: Invertible conditional gans for image editing (2016). arXiv preprint: arXiv:1611.06355
Creswell, A., Bharath, A.A.: Inverting the generator of a generative adversarial network (2016). arXiv preprint: arXiv:1611.05644
Donahue, J., Krahenbuhl, P., Darrell, T.: Adversarial feature learning (2016). arXiv preprint: arXiv:1605.09782
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., Courville, A.: Adversarially learned inference (2016). arXiv preprint: arXiv:1606.00704
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
Niu, X.M., Jiao, Y.H.: An overview of perceptual hashing. Acta Electronica Sin. 36(7), 1405–1411 (2008)
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)
Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA, vol. 1, p. 12 (1986)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks (2016). arXiv preprint: arXiv:1611.07004
Acknowledgments
This work was supported by the National Science Foundation of China (Grant No. 61375065 and 61625204), partially supported by the State Key Program of National Science Foundation of China (Grant No. 61432012 and 61432014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: Network Structures and Training Details
Appendix: Network Structures and Training Details
The Adam optimizer is used for all the experiments and the parameters are the same. The learning rate is 0.0002 and beta1 is 0.5. The experiment is trained on complete CelebA dataset. Batch size is 64 (Tables 3, 4, 5 and 6).
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Luo, J., Xu, Y., Tang, C., Lv, J. (2017). Learning Inverse Mapping by AutoEncoder Based Generative Adversarial Nets. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-70096-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70095-3
Online ISBN: 978-3-319-70096-0
eBook Packages: Computer ScienceComputer Science (R0)