  While GANs have shown success in realistic image generation, the idea of
using GANs for other tasks unrelated to synthesis is underexplored. Do GANs
learn meaningful structural parts of objects during their attempt to reproduce
those objects? In this work, we test this hypothesis and propose a simple and
effective approach based on GANs for semantic part segmentation that requires
as few as one label example along with an unlabeled dataset. Our key idea is to
leverage a trained GAN to extract pixel-wise representation from the input
image and use it as feature vectors for a segmentation network. Our experiments
demonstrate that GANs representation is "readily discriminative" and produces
surprisingly good results that are comparable to those from supervised
baselines trained with significantly more labels. We believe this novel
repurposing of GANs underlies a new class of unsupervised representation
learning that is applicable to many other tasks. More results are available at
https://repurposegans.github.io/.
