论文标题
改善图像到图像翻译中的样式内部分离
Improving Style-Content Disentanglement in Image-to-Image Translation
论文作者
论文摘要
近年来，无监督的图像到图像翻译方法取得了巨大的成功。但是，可以很容易地观察到他们的模型包含巨大的纠缠，这通常会损害翻译性能。在这项工作中，我们提出了一种有原则的方法，用于改善图像到图像翻译中的风格符合性分离。通过考虑到每个表示形式中的信息流，我们引入了一个额外的损失项，该损失术语可作为内容 - 底层。我们表明，我们的方法的结果比当前方法产生的结果明显得多，同时进一步提高了视觉质量和翻译多样性。
Unsupervised image-to-image translation methods have achieved tremendous success in recent years. However, it can be easily observed that their models contain significant entanglement which often hurts the translation performance. In this work, we propose a principled approach for improving style-content disentanglement in image-to-image translation. By considering the information flow into each of the representations, we introduce an additional loss term which serves as a content-bottleneck. We show that the results of our method are significantly more disentangled than those produced by current methods, while further improving the visual quality and translation diversity.
