Generative Adversarial Networks (GANs) have recently advanced image synthesis
by learning the underlying distribution of the observed data. However, how the
features learned from solving the task of image generation are applicable to
other vision tasks remains seldom explored. In this work, we show that learning
to synthesize images can bring remarkable hierarchical visual features that are
generalizable across a wide range of applications. Specifically, we consider
the pre-trained StyleGAN generator as a learned loss function and utilize its
layer-wise representation to train a novel hierarchical encoder. The visual
feature produced by our encoder, termed as Generative Hierarchical Feature
(GH-Feat), has strong transferability to both generative and discriminative
tasks, including image editing, image harmonization, image classification, face
verification, landmark detection, and layout prediction. Extensive qualitative
and quantitative experimental results demonstrate the appealing performance of
GH-Feat.

通过学习样本数据的潜在分布，生成对抗网络（GAN）最近推动了图像合成的进展。本文通过使用预训练的 StyleGAN 生成器作为已学习的损失函数，并利用其分层表示来训练新的层次编码器，展示了学习图像合成如何产生通用于广泛应用的出色分层视觉特征。通过广泛的定性和定量实验结果表明，GH-Feat 具有很强的可转移性，可以应用于图像编辑、图像谐调、图像分类、脸部验证、地标检测和布局预测等生成和判别任务。