Incremental learning with unlabeled data in the wild

Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee

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

Abstract

We propose to leverage a continuous and large stream of unlabeled data in the wild to alleviate catastrophic forgetting in class-incremental learning. Our experimental results on CIFAR and ImageNet datasets demonstrate the superiority of the proposed methods over prior methods: compared to the state-of-the-art method, our proposed method shows up to 14.9% higher accuracy and 45.9% less forgetting.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages29-32
Number of pages4
ISBN (Electronic)9781728125060
Publication statusPublished - 2019 Jun
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: 2019 Jun 162019 Jun 20

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/TerritoryUnited States
CityLong Beach
Period19/6/1619/6/20

Bibliographical note

Publisher Copyright:
© 2019 IEEE Computer Society. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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