DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Zili Yi,Hao Zhang,Ping Tan,Minglun Gong +3 more
- 01 Oct 2017
- pp 2868-2876
TL;DR: DualGAN as mentioned in this paper learns to translate images from domain U to those in domain V, while the dual GAN learns to invert the task, which enables image translators to be trained from two sets of unlabeled images from two domains.
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Abstract: Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently [7, 8, 21, 12, 4, 18]. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical, and large quantities of data may not always be available. Inspired by dual learning from natural language translation [23], we develop a novel dual-GAN mechanism, which enables image translators to be trained from two sets of unlabeled images from two domains. In our architecture, the primal GAN learns to translate images from domain U to those in domain V, while the dual GAN learns to invert the task. The closed loop made by the primal and dual tasks allows images from either domain to be translated and then reconstructed. Hence a loss function that accounts for the reconstruction error of images can be used to train the translators. Experiments on multiple image translation tasks with unlabeled data show considerable performance gain of DualGAN over a single GAN. For some tasks, DualGAN can even achieve comparable or slightly better results than conditional GAN trained on fully labeled data.
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Citations
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
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TL;DR: In this paper , a structure consistency constraint (SCC) is proposed to promote the consistency of image structures by reducing the randomness of color transformation in the translation process, which can be easily incorporated into most existing translation models.
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- 23 Aug 2020
TL;DR: This work translates between the deepest layers of a pre-trained network, where the deep features contain more semantics, and applies the translation between these deep features, in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy.
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Learning a Compact Vein Discrimination Model With GANerated Samples
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