论文标题
Ganilla：用于图像到图像翻译的生成对抗网络
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
论文作者
论文摘要
在本文中，我们探讨了儿童读物中的插图，作为未配对的图像到图像翻译中的新领域。我们表明，尽管当前的最新图像到图像翻译模型成功地传输了样式或内容，但它们无法同时传输这两个。我们提出了一个新的生成器网络来解决此问题，并表明所得网络在样式和内容之间取得了更好的平衡。 对于未配对的图像到图像翻译，没有明确或商定的评估指标。到目前为止，图像翻译模型的成功基于对有限数量的图像的主观定性视觉比较。为了解决这个问题，我们为图像到插图模型的定量评估提供了一个新的框架，其中使用单独的分类器考虑了内容和样式。在这个新的评估框架中，我们提出的模型的性能优于插图数据集中当前的最新模型。可以在https://github.com/giddyyupp/ganilla上找到我们的代码和预算模型。
In this paper, we explore illustrations in children's books as a new domain in unpaired image-to-image translation. We show that although the current state-of-the-art image-to-image translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content. There are no well-defined or agreed-upon evaluation metrics for unpaired image-to-image translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current state-of-the-art models on the illustrations dataset. Our code and pretrained models can be found at https://github.com/giddyyupp/ganilla.
