  Feature extraction with convolutional neural networks (CNNs) is a popular
method to represent images for machine learning tasks. These representations
seek to capture global image content, and ideally should be independent of
geometric transformations. We focus on measuring and visualizing the shift
invariance of extracted features from popular off-the-shelf CNN models. We
present the results of three experiments comparing representations of millions
of images with exhaustively shifted objects, examining both local invariance
(within a few pixels) and global invariance (across the image frame). We
conclude that features extracted from popular networks are not globally
invariant, and that biases and artifacts exist within this variance.
Additionally, we determine that anti-aliased models significantly improve local
invariance but do not impact global invariance. Finally, we provide a code
repository for experiment reproduction, as well as a website to interact with
our results at https://jakehlee.github.io/visualize-invariance.
