有条件的图像到图像翻译 (Conditional Image-to-Image Translation)

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control the translated results in the target domain and their results usually lack of diversity in the sense that a fixed image usually leads to (almost) deterministic translation result. In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain. It requires that the generated image should inherit some domain-specific features of the conditional image from the target domain. Therefore, changing the conditional image in the target domain will lead to diverse translation results for a fixed input image from the source domain, and therefore the conditional input image helps to control the translation results. We tackle this problem with unpaired data based on GANs and dual learning. We twist two conditional translation models (one translation from A domain to B domain, and the other one from B domain to A domain) together for inputs combination and reconstruction while preserving domain independent features. We carry out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations. The results demonstrate the effectiveness of our proposed method.

翻译：图像到图像翻译任务已经通过General Adversarial Networks(GANs)和双重学习广泛调查,但是,现有模型缺乏控制目标域翻译结果的能力,其结果通常缺乏多样性,因为固定图像通常导致(几乎)确定翻译结果。在本文中,我们研究一个新问题,即将图像到图像的有条件翻译,即将图像从源域翻译到目标域中以特定图像为条件的目标域。它要求生成的图像应当从目标域中继承有条件图像的某些特定域特性。因此,改变目标域中的有条件图像将导致从源域中获取固定输入图像的多种翻译结果,因此,有条件的输入图像有助于控制翻译结果。我们用基于GANs和双重学习的未加密数据解决这个问题。我们把两个有条件翻译模型(从一个域翻译到B域,另一个域到一个域)一起进行投入组合和重建,同时保存独立域域特性。因此,改变目标域域中的有条件图像将会导致源域域域域域中固定输入结果的多种翻译结果,因此,而有条件的输入图像图像图像图像图像图像图像图像图像图像有助于控制翻译结果。我们从性别与妇女之间展示。