论文标题
使用潜在空间映射的半监督图像到图像翻译
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
论文作者
论文摘要
由于捕获或标记大量配对数据的昂贵成本，最近的图像到图像翻译工作已从监督到无监督的设置。但是，使用周期矛盾约束的当前无监督方法可能找不到所需的映射，尤其是对于困难的翻译任务。另一方面，通常可以访问少量的配对数据。因此，我们引入了半监督图像翻译的一般框架。与以前的作品不同，我们的主要思想是学习潜在特征空间而不是图像空间的翻译。由于具有低维特征空间，更容易找到所需的映射功能，从而提高了翻译结果的质量以及翻译模型的稳定性。从经验上讲，我们表明，即使使用一些配对数据，也可以使用特征翻译产生更好的结果。与最先进方法的实验比较证明了拟议框架对各种具有挑战性的图像到图像翻译任务的有效性
Recent image-to-image translation works have been transferred from supervised to unsupervised settings due to the expensive cost of capturing or labeling large amounts of paired data. However, current unsupervised methods using the cycle-consistency constraint may not find the desired mapping, especially for difficult translation tasks. On the other hand, a small number of paired data are usually accessible. We therefore introduce a general framework for semi-supervised image translation. Unlike previous works, our main idea is to learn the translation over the latent feature space instead of the image space. Thanks to the low dimensional feature space, it is easier to find the desired mapping function, resulting in improved quality of translation results as well as the stability of the translation model. Empirically we show that using feature translation generates better results, even using a few bits of paired data. Experimental comparisons with state-of-the-art approaches demonstrate the effectiveness of the proposed framework on a variety of challenging image-to-image translation tasks
