  Recent generative models can synthesize "views" of artificial images that
mimic real-world variations, such as changes in color or pose, simply by
learning from unlabeled image collections. Here, we investigate whether such
views can be applied to real images to benefit downstream analysis tasks such
as image classification. Using a pretrained generator, we first find the latent
code corresponding to a given real input image. Applying perturbations to the
code creates natural variations of the image, which can then be ensembled
together at test-time. We use StyleGAN2 as the source of generative
augmentations and investigate this setup on classification tasks involving
facial attributes, cat faces, and cars. Critically, we find that several design
decisions are required towards making this process work; the perturbation
procedure, weighting between the augmentations and original image, and training
the classifier on synthesized images can all impact the result. Currently, we
find that while test-time ensembling with GAN-based augmentations can offer
some small improvements, the remaining bottlenecks are the efficiency and
accuracy of the GAN reconstructions, coupled with classifier sensitivities to
artifacts in GAN-generated images.
