Rethinking the Truly Unsupervised Image-to-Image Translation

• URL: http://arxiv.org/abs/2006.06500v2
• Date: Fri, 20 Aug 2021 03:36:26 GMT
• Title: Rethinking the Truly Unsupervised Image-to-Image Translation
• Authors: Kyungjune Baek, Yunjey Choi, Youngjung Uh, Jaejun Yoo, Hyunjung Shim
• Abstract summary: Unsupervised image-to-image translation model (TUNIT) learns to separate image domains and translates input images into estimated domains. Experimental results show TUNIT achieves comparable or even better performance than the set-level supervised model trained with full labels. TUNIT can be easily extended to semi-supervised learning with a few labeled data.
• Score: 29.98784909971291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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