Authors
Zili Yi, Hao Zhang, Ping Tan, Minglun Gong
Memorial University of Newfoundland; Simon Fraser University
Portals
Summary
In this paper, we aim to develop an unsupervised learning framework for general-purpose image-to-image translation, which only relies on unlabeled image data, such as two sets of photos and sketches for the photo-to-sketch conversion task. The obvious technical challenge is how to train a translator without any data characterizing correct translations. Our approach is inspired by dual learning from natural language processing. Dual learning trains two “opposite” language translators (e.g., English-to-French and French-to-English) simultaneously by minimizing the reconstruction loss resulting from a nested application of the two translators. The two translators represent a primal-dual pair and the nested application forms a closed loop, allowing the application of reinforcement learning. Specifically, the reconstruction loss measured over monolingual data (either English or French) would generate informative feedback to train a bilingual translation model.
Abstract
Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. 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, 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.
Contribution
- Our work develops a dual learning framework for imageto-image translation for the first time
Related Works
Dual learning