In this short report, we present a simple, yet effective approach to editing real images via generative adversarial networks (GAN). Unlike previous techniques, that treat all editing tasks as an operation that affects pixel values in the entire image in our approach we cut up the image into a set of smaller segments. For those segments corresponding latent codes of a generative network can be estimated with greater accuracy due to the lower number of constraints. When codes are altered by the user the content in the image is manipulated locally while the rest of it remains unaffected. Thanks to this property the final edited image better retains the original structures and thus helps to preserve natural look.

翻译：在这份简短的报告中,我们提出了一个简单而有效的方法,通过基因对抗网络(GAN)编辑真实图像。 与以前的技术不同,我们将所有编辑任务都视为影响整个图像像素值的操作,我们的方法将图像分割成一组较小的部分。对于这些部分,由于限制数量较少,可以更准确地估计基因网络相应的潜在代码。当用户修改代码时,图像的内容在本地操作,而其余部分则不受影响。由于这一特性,最终编辑图像更好地保留了原始结构,从而有助于保持自然外观。