NeuroView: Explainable Deep Network Decision Making

Simple item page
dc.contributor.advisorBaraniuk, Richard Gen_US
dc.contributor.committeeMemberBalakrishnan, Guhaen_US
dc.creatorBarberan, CJen_US
dc.date.accessioned2022-09-26T19:28:41Zen_US
dc.date.available2022-09-26T19:28:41Zen_US
dc.date.created2022-05en_US
dc.date.issued2022-07-06en_US
dc.date.submittedMay 2022en_US
dc.date.updated2022-09-26T19:28:41Zen_US
dc.description.abstractDeep 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. A deep network’s prediction cannot be explained in a formal mathematical manner such that you know how all the parameters contribute to the decision. NeuroView is a new family of DN architectures that are explainable by design. Each member of the family is derived from a standard DN architecture by concatenating all of the activations 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 multiple datasets and classification tasks to show that it has on par performance to a typical DN. Also, we inspect how it’s unit/class mapping aids in understanding the decision-making process. In this thesis, we propose using NeuroView in other architectures such as convolutional and recurrent neural networks to show how it can aid in providing additional understanding in applications that need more explanation.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBarberan, CJ. "NeuroView: Explainable Deep Network Decision Making." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113400">https://hdl.handle.net/1911/113400</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/113400en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectdeep learningen_US
dc.subjectexplainabilityen_US
dc.subjectinterpretabilityen_US
dc.subjectcomputer visionen_US
dc.titleNeuroView: Explainable Deep Network Decision Makingen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
BARBERAN-DOCUMENT-2022.pdf
Size:
18.19 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.6 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Theses and Dissertations