  Deep neural networks (DNs) provide superhuman performance in numerous
computer vision tasks, yet it remains unclear exactly which of a DN's units
contribute to a particular decision. NeuroView is a new family of DN
architectures that are interpretable/explainable by design. Each member of the
family is derived from a standard DN architecture by vector quantizing the unit
output values and feeding them into a global linear classifier. The resulting
architecture establishes a direct, causal link between the state of each unit
and the classification decision. We validate NeuroView on standard datasets and
classification tasks to show that how its unit/class mapping aids in
understanding the decision-making process.
