Domain adaptation by mixture of alignments of second- or higher-order scatter tensors

Piotr Koniusz, Yusuf Tas, Fatih Porikli

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    93 Citations (Scopus)

    Abstract

    In this paper, we propose an approach to the domain adaptation, dubbed Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second- or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second- or even higher-order scatter tensors; one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains) and RGB-D combined with Caltech256 (depth-to-rgb transfer). We attain state-of-the-art results.

    Original languageEnglish
    Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages7139-7148
    Number of pages10
    ISBN (Electronic)9781538604571
    DOIs
    Publication statusPublished - 6 Nov 2017
    Event30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States
    Duration: 21 Jul 201726 Jul 2017

    Publication series

    NameProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
    Volume2017-January

    Conference

    Conference30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
    Country/TerritoryUnited States
    CityHonolulu
    Period21/07/1726/07/17

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