pix2pix

Pix2Pix利用成对的图片进行图像翻译，即输入为同一张图片的两种不同风格，可用于进行风格迁移。研究条件对抗网络作为图像到图像转换问题的通用解决方案。这些网络不仅学习从输入图像到输出图像的映射，还学习了一个损失函数来训练这个映射。这使得将相同的通用方法应用于传统上需要非常不同的损失公式的问题成为可能。我们证明了这种方法在从标签图合成照片、从边缘图重建对象以及对图像进行着色等任务方面是有效的。

Pix2Pix performs image translation using paired images, where the inputs are two different stylized versions of the same image, enabling style transfer tasks. Conditional adversarial networks are studied as a general solution to the image-to-image translation problem. Such networks not only learn the mapping from input images to output images, but also learn a loss function to train this mapping. This makes it possible to apply the same universal approach to problems that traditionally required markedly distinct loss formulations. We validate the efficacy of this approach on tasks including photograph synthesis from label maps, object reconstruction from edge maps, and image colorization.

搜集汇总

背景与挑战

背景概述

Pix2Pix是一个用于图像到图像转换的数据集，专注于利用成对图片进行风格迁移，例如从标签图合成照片或从边缘图重建对象。它基于条件对抗网络，提供通用解决方案，适用于多种计算机视觉任务，由University of California, Berkeley于2017年发布，并包含相关开源工具和论文资源。