论文标题
步态：梯度调整后的无监督图像到图像翻译
GAIT: Gradient Adjusted Unsupervised Image-to-Image Translation
论文作者
论文摘要
图像到图像翻译（IIT）最近随着对抗性学习的发展取得了很大进步。在最近的大多数工作中，使用对抗性损失来匹配翻译和目标图像集的分布。但是，如果两个域具有不同的边缘分布，例如在统一区域，这可能会产生伪像。在这项工作中，我们提出了一种无监督的IIT方法，该方法在翻译后保留了统一区域。利用了目标图像的SOBEL响应与源图像的调整后的SOBEL响应之间的L2规范。所提出的方法已在水母到Haeckel数据集上进行了验证，该数据集准备演示上述问题，其中包含具有不同背景分布的图像。我们证明，与基线方法在定性和定量上相比，我们的方法获得了性能增长，显示了所提出的方法的有效性。
Image-to-image translation (IIT) has made much progress recently with the development of adversarial learning. In most of the recent work, an adversarial loss is utilized to match the distributions of the translated and target image sets. However, this may create artifacts if two domains have different marginal distributions, for example, in uniform areas. In this work, we propose an unsupervised IIT method that preserves the uniform regions after the translation. The gradient adjustment loss, which is the L2 norm between the Sobel response of the target image and the adjusted Sobel response of the source images, is utilized. The proposed method is validated on the jellyfish-to-Haeckel dataset, which is prepared to demonstrate the mentioned problem, which contains images with different background distributions. We demonstrate that our method obtained a performance gain compared to the baseline method qualitatively and quantitatively, showing the effectiveness of the proposed method.
