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Unsupervised Exemplar-Domain Aware Image-to-Image Translation
Entropy ( IF 2.7 ) Pub Date : 2021-05-02 , DOI: 10.3390/e23050565
Yuanbin Fu , Jiayi Ma , Xiaojie Guo

Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion. To this end, we design a novel deep translator, namely exemplar-domain aware image-to-image translator (EDIT for short). From a logical perspective, the translator needs to perform two main functions, i.e., feature extraction and style transfer. With consideration of logical network partition, the generator of our EDIT comprises of a part of blocks configured by shared parameters, and the rest by varied parameters exported by an exemplar-domain aware parameter network, for explicitly imitating the functionalities of extraction and mapping. The principle behind this is that, for images from multiple domains, the content features can be obtained by an extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars). In addition, a discriminator is equipped during the training phase to guarantee the output satisfying the distribution of the target domain. Our EDIT can flexibly and effectively work on multiple domains and arbitrary exemplars in a unified neat model. We conduct experiments to show the efficacy of our design, and reveal its advances over other state-of-the-art methods both quantitatively and qualitatively.

中文翻译:

无监督的示例域感知图像到图像转换

图像到图像转换用于将某种样式的图像转换为目标样式中的另一种,并保留原始内容。所需的翻译器应能够以可控的多对多方式生成各种结果。为此,我们设计了一种新颖的深度翻译器,即示例域感知图像到图像翻译器(简称EDIT)。从逻辑角度看,翻译器需要执行两个主要功能,即特征提取和样式转换。考虑到逻辑网络分区,我们的EDIT生成器包括一部分由共享参数配置的块,其余部分由示例域感知参数网络导出的各种参数构成,以明确地模仿提取和映射的功能。这背后的原理是,对于来自多个域的图像,内容特征可以通过提取器获得,而(重新)样式化是通过将提取的特征专门映射到不同的目的(域和示例)来实现的。另外,在训练阶段配备了鉴别器,以确保输出满足目标域的分布。我们的EDIT可以在统一整齐的模型中灵活有效地处理多个领域和任意示例。我们进行实验以展示我们设计的功效,并定量和定性地揭示其相对于其他最新技术的进步。在训练阶段配备了一个鉴别器,以确保输出满足目标域的分布。我们的EDIT可以在统一整齐的模型中灵活有效地处理多个领域和任意示例。我们进行实验以展示我们设计的功效,并定量和定性地揭示其相对于其他最新技术的进步。在训练阶段配备了一个鉴别器,以确保输出满足目标域的分布。我们的EDIT可以在统一整齐的模型中灵活有效地处理多个领域和任意示例。我们进行实验以展示我们设计的功效,并定量和定性地揭示其相对于其他最新技术的进步。
更新日期:2021-05-03
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