Holistic Label Correction for Noisy Multi-Label Classification

Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

8 Citations (Scopus)

Abstract

Multi-label classification aims to learn classification models from instances associated with multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels in multi-label classification. As a result of today's big and complex data, noisy labels are inevitable, making it looming to target multi-label classification with noisy labels. Although the importance of label dependence has been shown in multi-label classification with clean labels, it is challenging and hard to bring label dependence to the problem of multi-label classification with noisy labels. The issues are, that we do not understand why label dependence is helpful in the problem, and how to learn and utilize label dependence only using training data with noisy multiple labels. In this paper, we bring label dependence to tackle the problem of multi-label classification with noisy labels. Specifically, we first provide a high-level understanding of why label dependence helps distinguish the examples with clean/noisy multiple labels. Benefiting from the memorization effect in handling noisy labels, a novel algorithm is then proposed to learn the label dependence by only employing training data with noisy multiple labels, and utilize the learned dependence to help correct noisy multiple labels to clean ones. We prove that the use of label dependence could bring a higher success rate for recovering correct multiple labels. Empirical evaluations justify our claims and demonstrate the superiority of our algorithm.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PublisherIEEE
Pages1483-1493
Number of pages11
ISBN (Electronic)9798350307184
ISBN (Print)9798350307191
DOIs
Publication statusPublished - 1 Oct 2023
Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
Duration: 2 Oct 20236 Oct 2023
https://iccv2023.thecvf.com/ (Conference website)
https://ieeexplore.ieee.org/xpl/conhome/10376473/proceeding (Conference proceedings)

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Country/TerritoryFrance
CityParis
Period2/10/236/10/23
Internet address

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition

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