  Though anomaly detection (AD) can be viewed as a classification problem
(nominal vs. anomalous) it is usually treated in an unsupervised manner since
one typically does not have access to, or it is infeasible to utilize, a
dataset that sufficiently characterizes what it means to be "anomalous." In
this paper we present results demonstrating that this intuition surprisingly
seems not to extend to deep AD on images. For a recent AD benchmark on
ImageNet, classifiers trained to discern between normal samples and just a few
(64) random natural images are able to outperform the current state of the art
in deep AD. Experimentally we discover that the multiscale structure of image
data makes example anomalies exceptionally informative.
