Iterative Binary Decisions

Stephan Alaniz, Zeynep Akata

Abstract: The complexity of functions a neural network approximates make it hard to explain what the classification decision is based on. In this work, we present a framework that exposes more information about this decision-making process. Instead of producing a classification in a single step, our model iteratively makes binary sub-decisions which, when combined as a whole, ultimately produces the same classification result while revealing a decision tree as thought process. While there is generally a trade-off between interpretability and accuracy, the insights our model generates come at a negligible loss in accuracy. The decision tree resulting from the sequence of binary decisions of our model reveal a hierarchical clustering of the data and can be used as learned attributes in zero-shot learning.

Keywords: explainable AI, interpretability, deep learning, decision tree, zero-shot learning