VaB-AL: incorporating class imbalance and difficulty with variational Bayes for active learning

Jongwon Choi, Kwang Moo Yi, Jihoon Kim, Jinho Choo, Byoungjip Kim, Jinyeop Chang, Youngjune Gwon, Hyung Jin Chang

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

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Abstract

Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes’ rule, that can naturally incorporate class imbalance into the Active Learning framework. We derive that three terms should be considered together when estimating the probability of a classifier making a mistake for a given sample; i) probability of mislabelling a class, ii) likelihood of the data given a predicted class, and iii) the prior probability on the abundance of a predicted class. Implementing these terms requires a generative model and an intractable likelihood estimation. Therefore, we train a Variational Auto Encoder (VAE) for this purpose. To further tie the VAE with the classifier and facilitate VAE training, we use the classifiers’ deep feature representations as input to the VAE. By considering all three probabilities, among them, especially the data imbalance, we can substantially improve the potential of existing methods under limited data budget. We show that our method can be applied to classification tasks on multiple different datasets – including one that is a real-world dataset with heavy data imbalance – significantly outperforming the state of the art.
Original languageEnglish
Title of host publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
Pages6745 - 6754
ISBN (Electronic)978-1-6654-4509-2
ISBN (Print)978-1-6654-4510-8
DOIs
Publication statusPublished - 2 Nov 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Nashville, United States
Duration: 21 Jun 202124 Jun 2021

Publication series

NameProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
PublisherIEEE
ISSN (Electronic)2575-7075

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2021
Country/TerritoryUnited States
CityNashville
Period21/06/2124/06/21

Keywords

  • training
  • learning systems
  • computer vision
  • estimation
  • object detection
  • pattern recognition
  • task analysis

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