Systems that perform image manipulation using deep convolutional networks have achieved remarkable realism. Perceptual losses and losses based on adversarial discriminators are the two main classes of learning objectives behind these advances. In this work, we show how these two ideas can be combined in a principled and non-additive manner for unaligned image translation tasks. This is accomplished through a special architecture of the discriminator network inside generative adversarial learning framework. The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network. The resulting architecture can be trained on unaligned image datasets while benefiting from the robustness and efficiency of perceptual losses. We demonstrate the merits of the new architecture in a series of qualitative and quantitative comparisons with baseline approaches and state-of-the-art frameworks for unaligned image translation.

本文介绍了一种将感知损失和基于对抗鉴别器的学习目标通过一个特殊的鉴别器网络框架结合起来，在未对齐的图像数据集上训练获得准确快速结果的新体系结构——感知鉴别器。通过此体系结构在未对齐的图像翻译任务中取得了良好效果。

利用感知辨别器进行图像操作