  Automated detection of new, interesting, unusual, or anomalous images within
large data sets has great value for applications from surveillance (e.g.,
airport security) to science (observations that don't fit a given theory can
lead to new discoveries). Many image data analysis systems are turning to
convolutional neural networks (CNNs) to represent image content due to their
success in achieving high classification accuracy rates. However, CNN
representations are notoriously difficult for humans to interpret. We describe
a new strategy that combines novelty detection with CNN image features to
achieve rapid discovery with interpretable explanations of novel image content.
We applied this technique to familiar images from ImageNet as well as to a
scientific image collection from planetary science.
