  We aim to build image generation models that generalize to new domains from
few examples. To this end, we first investigate the generalization properties
of classic image generators, and discover that autoencoders generalize
extremely well to new domains, even when trained on highly constrained data. We
leverage this insight to produce a robust, unsupervised few-shot image
generation algorithm, and introduce a novel training procedure based on
recovering an image from data augmentations. Our Augmentation-Interpolative
AutoEncoders synthesize realistic images of novel objects from only a few
reference images, and outperform both prior interpolative models and supervised
few-shot image generators. Our procedure is simple and lightweight, generalizes
broadly, and requires no category labels or other supervision during training.
