Striking the right balance with uncertainty

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

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

21 Citations (Scopus)

Abstract

Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes tend to get a concentrated representation in the classification space which hampers the generalization of learned boundaries to new test examples. In this paper, we demonstrate that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples. Subsequently, we present a novel framework for uncertainty based class imbalance learning that follows two key insights: First, classification boundaries should be extended further away from a more uncertain (rare) class to avoid over-fitting and enhance its generalization. Second, each sample should be modeled as a multi-variate Gaussian distribution with a mean vector and a covariance matrix defined by the sample's uncertainty. The learned boundaries should respect not only the individual samples but also their distribution in the feature space. Our proposed approach efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers. We systematically study the class imbalance problem and derive a novel loss formulation for max-margin learning based on Bayesian uncertainty measure. The proposed method shows significant performance improvements on six benchmark datasets for face verification, attribute prediction, digit/object classification and skin lesion detection.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
EditorsAbhinav Gupta, Derek Hoiem, Gang Hua, Zhuowen Tu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages103-112
Number of pages10
ISBN (Electronic)9781728132938
ISBN (Print)9781728132945
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2019 - Long Beach, United States of America
Duration: 16 Jun 201920 Jun 2019
Conference number: 32nd
http://cvpr2019.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/8938205/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2019-June
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2019
Abbreviated titleCVPR 2019
CountryUnited States of America
CityLong Beach
Period16/06/1920/06/19
Internet address

Keywords

  • Deep Learning
  • Low-level Vision

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