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NeuroView: Explainable Deep Network Decision Making

2021-10-15 00:00:59
CJ Barberan, Randall Balestriero, Richard G. Baraniuk
arXiv_CV
   
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.07778

PDF

https://arxiv.org/pdf/2110.07778.pdf

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