mlsanityData to 7 Oct 2026

NeuroView: Explainable Deep Network Decision Making

arXiv 2110.07778 ↗First posted 15 Oct 2021cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

26 of 40 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022NeuroView-RNN: It's About Timecited by 2