publication . Preprint . 2018

Learning image-to-image translation using paired and unpaired training samples

Tripathy, Soumya; Kannala, Juho; Rahtu, Esa;
Open Access English
  • Published: 08 May 2018
Abstract
Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been proposed depending on whether aligned image pairs or two sets of (unaligned) examples from both domains are available for training. While paired training samples might be difficult to obtain, the unpaired setup leads to a highly under-constrained problem and inferior results. In this paper, we propose a new general purpose image-to-image translation model that is able to utilize both paired and unpaired training data simultaneously. We compare o...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Related Organizations
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37 references, page 1 of 3

1. Zhang, R., Zhu, J.Y., Isola, P., Geng, X., Lin, A.S., Yu, T., Efros, A.A.: Realtime user-guided image colorization with learned deep priors. arXiv preprint arXiv:1705.02999 (2017)

2. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (July 2017) 5967{5976

3. Sangkloy, P., Lu, J., Fang, C., Yu, F., Hays, J.: Scribbler: Controlling deep image synthesis with sketch and color. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Volume 2. (2017)

4. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). (Oct 2017) 2242{2251

5. 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. (2014) 2672{2680

6. 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. (2016) 2536{2544

7. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image superresolution using a generative adversarial network. arXiv preprint (2016)

8. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, Springer (2016) 649{666

9. Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017)

10. Yi, Z., Zhang, H., Tan, P., Gong, M.: Dualgan: Unsupervised dual learning for image-to-image translation. arXiv preprint (2017)

11. Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in neural information processing systems. (2016) 469{477

12. Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems. (2017) 700{708

13. Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Moressi, I., Cole, F., Murphy, K.: Xgan: Unsupervised image-to-image translation for many-to-many mappings. arXiv preprint arXiv:1711.05139 (2017) [OpenAIRE]

14. Hoshen, Y., Wolf, L.: Nam-unsupervised cross-domain image mapping without cycles or gans. (2018) [OpenAIRE]

15. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2015) 3431{3440

37 references, page 1 of 3
Abstract
Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been proposed depending on whether aligned image pairs or two sets of (unaligned) examples from both domains are available for training. While paired training samples might be difficult to obtain, the unpaired setup leads to a highly under-constrained problem and inferior results. In this paper, we propose a new general purpose image-to-image translation model that is able to utilize both paired and unpaired training data simultaneously. We compare o...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Related Organizations
Download from
37 references, page 1 of 3

1. Zhang, R., Zhu, J.Y., Isola, P., Geng, X., Lin, A.S., Yu, T., Efros, A.A.: Realtime user-guided image colorization with learned deep priors. arXiv preprint arXiv:1705.02999 (2017)

2. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (July 2017) 5967{5976

3. Sangkloy, P., Lu, J., Fang, C., Yu, F., Hays, J.: Scribbler: Controlling deep image synthesis with sketch and color. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Volume 2. (2017)

4. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). (Oct 2017) 2242{2251

5. 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. (2014) 2672{2680

6. 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. (2016) 2536{2544

7. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image superresolution using a generative adversarial network. arXiv preprint (2016)

8. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, Springer (2016) 649{666

9. Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017)

10. Yi, Z., Zhang, H., Tan, P., Gong, M.: Dualgan: Unsupervised dual learning for image-to-image translation. arXiv preprint (2017)

11. Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in neural information processing systems. (2016) 469{477

12. Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems. (2017) 700{708

13. Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Moressi, I., Cole, F., Murphy, K.: Xgan: Unsupervised image-to-image translation for many-to-many mappings. arXiv preprint arXiv:1711.05139 (2017) [OpenAIRE]

14. Hoshen, Y., Wolf, L.: Nam-unsupervised cross-domain image mapping without cycles or gans. (2018) [OpenAIRE]

15. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2015) 3431{3440

37 references, page 1 of 3
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