  Despite an impressive performance from the latest GAN for generating
hyper-realistic images, GAN discriminators have difficulty evaluating the
quality of an individual generated sample. This is because the task of
evaluating the quality of a generated image differs from deciding if an image
is real or fake. A generated image could be perfect except in a single area but
still be detected as fake. Instead, we propose a novel approach for detecting
where errors occur within a generated image. By collaging real images with
generated images, we compute for each pixel, whether it belongs to the real
distribution or generated distribution. Furthermore, we leverage attention to
model long-range dependency; this allows detection of errors which are
reasonable locally but not holistically. For evaluation, we show that our error
detection can act as a quality metric for an individual image, unlike FID and
IS. We leverage Improved Wasserstein, BigGAN, and StyleGAN to show a ranking
based on our metric correlates impressively with FID scores. Our work opens the
door for better understanding of GAN and the ability to select the best samples
from a GAN model.
