  We explore the use of class-conditional autoregressive (CA) models to perform
image classification on MNIST-10. Autoregressive models assign probability to
an entire input by combining probabilities from each individual feature; hence
classification decisions made by a CA can be readily decomposed into
contributions from each each input feature. That is to say, CA are inherently
locally interpretable. Our experiments show that naively training a CA achieves
much worse accuracy compared to a standard classifier, however this is due to
over-fitting and not a lack of expressive power. Using knowledge distillation
from a standard classifier, a student CA can be trained to match the
performance of the teacher while still being interpretable.
