Teaching Categories to Human Learners with Visual Explanations

Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, Yisong Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student’s ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3820-3828
Number of pages9
ISBN (Electronic)978-1-5386-6420-9
ISBN (Print)978-1-5386-6421-6
DOIs
Publication statusPublished - 17 Dec 2018
Event2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018
http://cvpr2018.thecvf.com/

Publication series

Name
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Conference2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2018
Country/TerritoryUnited States
CitySalt Lake City
Period18/06/1822/06/18
Internet address

Keywords

  • computer aided instruction
  • teaching
  • class label
  • visual explanations
  • computer-assisted teaching
  • machine teaching
  • teaching framework
  • interpretable explanations
  • Education
  • Visualization
  • Task analysis
  • Adaptation models
  • Mathematical model
  • Computational modeling
  • Computer vision

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