  Recent years witness the tremendous success of generative adversarial
networks (GANs) in synthesizing photo-realistic images. GAN generator learns to
compose realistic images and reproduce the real data distribution. Through
that, a hierarchical visual feature with multi-level semantics spontaneously
emerges. In this work we investigate that such a generative feature learned
from image synthesis exhibits great potentials in solving a wide range of
computer vision tasks, including both generative ones and more importantly
discriminative ones. We first train an encoder by considering the pretrained
StyleGAN generator as a learned loss function. The visual features produced by
our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align
with the layer-wise GAN representations, and hence describe the input image
adequately from the reconstruction perspective. Extensive experiments support
the versatile transferability of GH-Feat across a range of applications, such
as image editing, image processing, image harmonization, face verification,
landmark detection, layout prediction, image retrieval, etc. We further show
that, through a proper spatial expansion, our developed GH-Feat can also
facilitate fine-grained semantic segmentation using only a few annotations.
Both qualitative and quantitative results demonstrate the appealing performance
of GH-Feat.
