Conditional Autoregressors are Interpretable Classifiers

Nathan Elazar

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arXiv · 2203.17002

Conditional Autoregressors are Interpretable Classifiers

Nathan Elazar

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Abstract

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.

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Nathan Elazar. 2022-03-31. Conditional Autoregressors are Interpretable Classifiers. https://arxiv.org/abs/2203.17002

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