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Cross Domain Image Transformation Using Effective Latent Space Association

  • Naeem Ul Islam
  • Sukhan LeeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Cross-domain image to image translation task aims at learning the joint distribution of images from marginal distributions in their respective domains. However, estimation of joint distribution from marginal distribution is a challenging problem as there is no, one to one correspondence. To address this problem, we propose a general approach based on variational autoencoders along with latent space association network (VAE-LSAN). The variational autoencoders learn the marginal distribution of the images in the individual domain whereas, the association network provides the correspondence between the marginal distributions of the cross domains. Our architecture effectively performs mapping of images in individual as well as cross domains. Experimental results show state of the art performance of our framework on different datasets in term of synthesizing images within its respective domain as well as cross domains.

Keywords

Generative models Variational autoencoder Association network 

Notes

Acknowledgments

Sukhan Lee proposed the concept of latent space association for image to image translation while Naeem Ul Islam implements the concept and carries out experimentation. This research was supported, in part, by the “3D Recognition Project” of Korea Evaluation Institute of Industrial Technology (KEIT) (10060160) and, in part, by the“Robot Industry Fusion Core Technology Development Project” of KEIT (10048320), sponsored by the Korea Ministry of Trade, Industry and Energy (MOTIE).

References

  1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
  2. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005. vol. 2, pp. 60–65. IEEE (2005)Google Scholar
  3. Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2photo: internet image montage. ACM Trans. Graphics (TOG) 28(5), 124 (2009)Google Scholar
  4. Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 415–423 (2015)Google Scholar
  5. Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: Emnist: an extension of mnist to handwritten letters. arXiv preprint arXiv:1702.05373 (2017)
  6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)Google Scholar
  7. Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486–1494 (2015)Google Scholar
  8. Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 341–346. ACM (2001)Google Scholar
  9. Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650–2658 (2015)Google Scholar
  10. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. In: ACM Transactions on Graphics (TOG). vol. 25, pp. 787–794. ACM (2006)Google Scholar
  11. 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
  12. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 327–340. ACM (2001)Google Scholar
  13. Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graphics (TOG) 35(4), 110 (2016)CrossRefGoogle Scholar
  14. Ioe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  15. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016)
  16. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  17. Kingma, D.P., Salimans, T., Welling, M.: Improving variational inference with inverse autoregressive flow. arXiv preprint arXiv:1606.04934 (2016)
  18. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
  19. Laffont, P.Y., Ren, Z., Tao, X., Qian, C., Hays, J.: Transient attributes for high-level understanding and editing of outdoor scenes. ACM Trans. Graphics (TOG) 33(4), 149 (2014)CrossRefGoogle Scholar
  20. Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)
  21. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 (2016)
  22. Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 1718–1727 (2015)Google Scholar
  23. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  24. Maaløe, L., Sønderby, C.K., Sønderby, S.K., Winther, O.: Auxiliary deep generative models. arXiv preprint arXiv:1602.05473 (2016)
  25. Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML Workshop on Deep Learning for Audio, Speech, and Language Processing (2013)Google Scholar
  26. Mihail, R.P., Workman, S., Bessinger, Z., Jacobs, N.: Sky segmentation in the wild: an empirical study. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–6 (2016)Google Scholar
  27. Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A., Clune, J.: Plug & play generative networks: conditional iterative generation of images in latent space. arXiv preprint arXiv:1612.00005 (2016)
  28. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, December 2008Google Scholar
  29. van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with pixelcnn decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)Google Scholar
  30. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)Google Scholar
  31. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  32. Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and variational inference in deep latent gaussian models. In: International Conference on Machine Learning (2014)Google Scholar
  33. Shih, Y., Paris, S., Durand, F., Freeman, W.T.: Data-driven hallucination of different times of day from a single outdoor photo. ACM Trans. Graphics (TOG) 32(6), 200 (2013)CrossRefGoogle Scholar
  34. Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. arXiv preprint arXiv:1612.07828 (2016)
  35. Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200 (2016)
  36. Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)Google Scholar
  37. Yan, X., Yang, J., Sohn, K., Lee, H.: Attribute2image: cimage generation from visual attributes. In: European Conference on Computer Vision, pp. 776–791. Springer (2016)Google Scholar
  38. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, pp. 649–666. Springer (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Intelligent Systems Research InstituteSungkyunkwan UniversitySeoulRepublic of Korea

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