论文标题
使用域有条件归一化的零对图像到图像翻译
Zero-Pair Image to Image Translation using Domain Conditional Normalization
论文作者
论文摘要
在本文中，我们提出了一种基于域条件归一化（DCN）的方法，用于零对图像到图像转换，即，在两个没有配对的训练数据的域之间翻译，但每个域都具有带有第三个域的配对训练数据。我们采用一个具有编码器码头结构的单个发电机，并分析了域条件归一化的不同实现，以获得所需的目标域输出。验证基准测试使用RGB深度对和RGB语义对训练，并比较深度语义翻译任务的性能。与比较的方法相比，所提出的方法在定性和定量术语中改善了，同时使用较少的参数。代码可在https://github.com/samarthshukla/dcn上找到
In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output. The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for training and compares performance for the depth-semantic translation task. The proposed approaches improve in qualitative and quantitative terms over the compared methods, while using much fewer parameters. Code available at https://github.com/samarthshukla/dcn
