Representation Learning in Anomaly Detection: Successes, Limits and a
  Grand Challenge
Yedid Hoshen
In this perspective paper, we argue that the dominant paradigm in anomaly
detection cannot scale indefinitely and will eventually hit fundamental limits.
This is due to the a no free lunch principle for anomaly detection. These
limitations can be overcome when there are strong tasks priors, as is the case
for many industrial tasks. When such priors do not exists, the task is much
harder for anomaly detection. We pose two such tasks as grand challenges for
anomaly detection: i) scientific discovery by anomaly detection ii) a
"mini-grand" challenge of detecting the most anomalous image in the ImageNet
dataset. We believe new anomaly detection tools and ideas would need to be
developed to overcome these challenges.