Rethinking the Truly Unsupervised Image-to-Image Translation

Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-level supervision can be a severe bottleneck for data collection in practice. In this paper, we tackle image-to-image transl...

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Bibliographic Details
Published in2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 14134 - 14143
Main Authors Baek, Kyungjune, Choi, Yunjey, Uh, Youngjung, Yoo, Jaejun, Shim, Hyunjung
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2021
Subjects
Codes
Computational modeling
Computer vision
Data collection
Image and video synthesis
Representation learning
Semisupervised learning
Task analysis
Transfer/Low-shot/Semi/Unsupervised Learning
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Summary:Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-level supervision can be a severe bottleneck for data collection in practice. In this paper, we tackle image-to-image translation in a fully unsupervised setting, i.e., neither paired images nor domain labels. To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains. Experimental results show that our model achieves comparable or even better performance than the set-level supervised model trained with full labels, generalizes well on various datasets, and is robust against the choice of hyperparameters (e.g. the preset number of pseudo domains). Furthermore, TUNIT can be easily extended to semi-supervised learning with a few labeled data.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.01389