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Generative Moment Matching Autoencoder with Perceptual Loss

  • Mohammad Ahangar Kiasari
  • Dennis Singh Moirangthem
  • Minho LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

In deep generative networks, one of the major challenges is to generate non-blurry, clearer images. Unlike the generative adversarial networks, generative models such as variational autoencoders, generative moment matching networks etc. use pixel-wise loss which leads to the generation of blurry images. In this paper, we propose an improved generative model called Generative Moment Matching Autoencoder (GMMA) with a feature-wise loss mechanism. We use a pre-trained VGGNet convolutional neural network to compute the loss at the various feature extraction layers. We evaluate the performance of our model on the MNIST and the Large-scale CelebFaces Attributes (CelebA) dataset. Our generative model outperforms the existing models on the log-likelihood estimation test. We also illustrate the effectiveness of our mechanism and the improved generation and reconstruction capabilities. The proposed GMMA with perceptual loss successfully alleviates the problem of blurry image generation.

Keywords

Generative Networks Moment Matching Autoencoder Convolutional Neural Networks Feature extraction 

Notes

Acknowledgments

This work was supported by the Industrial Strategic Technology Development Program (10044009) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (50%).

References

  1. 1.
    Bengio, Y., Mesnil, G., Dauphin, Y., Rifai, S.: Better mixing via deep representations. In: ICML (1), pp. 552–560 (2013)Google Scholar
  2. 2.
    Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 262–270 (2015)Google Scholar
  3. 3.
    Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint (2015). arXiv:1508.06576
  4. 4.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  5. 5.
    Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J., et al.: A Kernel method for the two-sample-problem. Adv. Neural Inf. Process. Syst. 19, 513 (2007)zbMATHGoogle Scholar
  6. 6.
    Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A Kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)zbMATHMathSciNetGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  8. 8.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Hou, X., Shen, L., Sun, K., Qiu, G.: Deep feature consistent variational autoencoder. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1133–1141. IEEE (2017)Google Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint, arXiv:1502.03167 (2015)
  11. 11.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint, arXiv:1412.6980 (2014)
  12. 12.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint, arXiv:1312.6114 (2013)
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  14. 14.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Swersky, K., Zemel, R.S.: Generative moment matching networks. In: ICML, pp. 1718–1727 (2015)Google Scholar
  16. 16.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)Google Scholar
  17. 17.
    Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. arXiv preprint, arXiv:1511.05644 (2015)
  18. 18.
    Nowozin, S., Cseke, B., Tomioka, R.: f-gan: training generative neural samplers using variational divergence minimization. In: Advances in Neural Information Processing Systems, pp. 271–279 (2016)Google Scholar
  19. 19.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint, arXiv:1511.06434 (2015)
  20. 20.
    Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. 1278–1286 (2014)Google Scholar
  21. 21.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint, arXiv:1409.1556 (2014)
  22. 22.
    Thibodeau-Laufer, E., Alain, G., Yosinski, J.: Deep generative stochastic networks trainable by backprop (2014)Google Scholar
  23. 23.
    Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2528–2535. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammad Ahangar Kiasari
    • 1
  • Dennis Singh Moirangthem
    • 1
  • Minho Lee
    • 1
    Email author
  1. 1.School of Electronics EngineeringKyungpook National UniversityDaeguSouth Korea

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