  Metrics for evaluating generative models aim to measure the discrepancy
between real and generated images. The often-used Frechet Inception Distance
(FID) metric, for example, extracts "high-level" features using a deep network
from the two sets. However, we find that the differences in "low-level"
preprocessing, specifically image resizing and compression, can induce large
variations and have unforeseen consequences. For instance, when resizing an
image, e.g., with a bilinear or bicubic kernel, signal processing principles
mandate adjusting prefilter width depending on the downsampling factor, to
antialias to the appropriate bandwidth. However, commonly-used implementations
use a fixed-width prefilter, resulting in aliasing artifacts. Such aliasing
leads to corruptions in the feature extraction downstream. Next, lossy
compression, such as JPEG, is commonly used to reduce the file size of an
image. Although designed to minimally degrade the perceptual quality of an
image, the operation also produces variations downstream. Furthermore, we show
that if compression is used on real training images, FID can actually improve
if the generated images are also subsequently compressed. This paper shows that
choices in low-level image processing have been an underappreciated aspect of
generative modeling. We identify and characterize variations in generative
modeling development pipelines, provide recommendations based on signal
processing principles, and release a reference implementation to facilitate
future comparisons.
