  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.
