Abstract
An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data.
J. Lin and Y. Pang—The first two authors contributed equally to this work.
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Notes
- 1.
In this paper, we refer to general UI2I as tasks where there are multiple images in the source and target domains, i.e., the translation tasks studied in [38].
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Acknowledgements
This work was supported in part by NSFC under Grant U1908209, 61632001 and the National Key Research and Development Program of China 2018AAA0101400. This work was also supported in part by NSF award IIS-1704337.
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Lin, J., Pang, Y., Xia, Y., Chen, Z., Luo, J. (2020). TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12349. Springer, Cham. https://doi.org/10.1007/978-3-030-58548-8_2
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