  Machine learning techniques can automatically identify outliers in massive
datasets, much faster and more reproducible than human inspection ever could.
But finding such outliers immediately leads to the question: which features
render this input anomalous? We propose a new feature attribution method,
Inverse Multiscale Occlusion, that is specifically designed for outliers, for
which we have little knowledge of the type of features we want to identify and
expect that the model performance is questionable because anomalous test data
likely exceed the limits of the training data. We demonstrate our method on
outliers detected in galaxy spectra from the Dark Energy Survey Instrument and
find its results to be much more interpretable than alternative attribution
approaches.
