Assistive signals for deep neural network classifiers

Research output: Chapter in Book/Conference paperConference paperpeer-review

2 Citations (Scopus)
15 Downloads (Pure)

Abstract

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model's confidence score regardless if it's under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. 'Assistive Signals' also illustrate bias of ML models towards certain patterns in real-life objects.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1221-1225
Number of pages5
ISBN (Electronic)9781665448994
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
Duration: 19 Jun 202125 Jun 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Country/TerritoryUnited States
CityVirtual, Online
Period19/06/2125/06/21

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