  We propose GAN-Supervised Learning, a framework for learning discriminative
models and their GAN-generated training data jointly end-to-end. We apply our
framework to the dense visual alignment problem. Inspired by the classic
Congealing method, our GANgealing algorithm trains a Spatial Transformer to map
random samples from a GAN trained on unaligned data to a common,
jointly-learned target mode. We show results on eight datasets, all of which
demonstrate our method successfully aligns complex data and discovers dense
correspondences. GANgealing significantly outperforms past self-supervised
correspondence algorithms and performs on-par with (and sometimes exceeds)
state-of-the-art supervised correspondence algorithms on several datasets --
without making use of any correspondence supervision or data augmentation and
despite being trained exclusively on GAN-generated data. For precise
correspondence, we improve upon state-of-the-art supervised methods by as much
as $3\times$. We show applications of our method for augmented reality, image
editing and automated pre-processing of image datasets for downstream GAN
training.
