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Becoming the expert - interactive multi-class machine teaching

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

Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)978-1-4673-6964-0
Publication statusPublished - 15 Oct 2015
Event2015 IEEE Conference on Computer Vision and Pattern Recognition - Boston, United States
Duration: 8 Jun 201510 Jun 2015

Publication series

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


Conference2015 IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2015
CountryUnited States
Internet address


Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later – or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student’s ability and progress, based on their correct and incorrect answers, produces better ‘experts’. We present results using real human participants across several varied and challenging real-world datasets.

    Research areas

  • computer aided instruction, image classification, image representation, interactive systems, teaching, interactive multiclass machine teaching algorithm, image categories, prior knowledge, teaching images, representative images, image-importance, visual concepts, adaptive algorithm, labeled images, teaching set, teaching strategy, student ability, student progress, Education, Visualization, Computational modeling, Computers, Testing, Adaptation models, Probabilistic logic


2015 IEEE Conference on Computer Vision and Pattern Recognition


Boston, United States

Event: Conference

ID: 122749259