Gaussian affinity for max-margin class imbalanced learning

Munawar Hayat, Salman Khan, Syed Waqas Zamir, Jianbing Shen, Ling Shao

    Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

    56 Citations (Scopus)


    Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an 'affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.

    Original languageEnglish
    Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages11
    ISBN (Electronic)9781728148038
    Publication statusPublished - Oct 2019
    Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
    Duration: 27 Oct 20192 Nov 2019

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499


    Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
    Country/TerritoryKorea, Republic of


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