Cited by 655Machine Learning and AlgorithmsDomain Adaptation and Few-Shot LearningMachine Learning and Data Classification

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The Power of Ensembles for Active Learning in Image Classification

Beluch, William H., Genewein, Tim, Nurnberger, Andreas, Kohler, Jan M.·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

DOI 10.1109/cvpr.2018.00976

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Abstract

Deep learning methods have become the de-facto standard for challenging image processing tasks such as image classification. One major hurdle of deep learning approaches is that large sets of labeled data are necessary, which can be prohibitively costly to obtain, particularly in medical image diagnosis applications. Active learning techniques can alleviate this labeling effort. In this paper we investigate some recently proposed methods for active learning with high-dimensional data and convolutional neural network classifiers. We compare ensemble-based methods against Monte-Carlo Dropout and geometric approaches. We find that ensembles perform better and lead to more calibrated predictive uncertainties, which are the basis for many active learning algorithms. To investigate why Monte-Carlo Dropout uncertainties perform worse, we explore potential differences in isolation in a series of experiments. We show results for MNIST and CIFAR-10, on which we achieve a test set accuracy of 90% with roughly 12,200 labeled images, and initial results on ImageNet. Additionally, we show results on a large, highly class-imbalanced diabetic retinopathy dataset. We observe that the ensemble-based active learning effectively counteracts this imbalance during acquisition.

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Authors: Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M.
Affiliations: Robert Bosch (United States) (US)
ORCID: Genewein, Tim
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher: IEEE
Published: 2018-06
Pages: 9368-9377
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2018.00976
OpenAlex: W2798820905
Cited by: 655
References: 64
Citations by year: 2026: 33 · 2025: 69 · 2024: 117 · 2023: 120 · 2022: 94 · 2021: 115 · 2020: 72 · 2019: 31 · 2018: 4
FWCI: 22.66
Type: conference paper
Language: en
Open access: closed
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords: active learning (machine learning), image classification, ensemble methods, Monte Carlo dropout, uncertainty estimation, convolutional neural networks, class imbalance, diabetic retinopathy, medical image diagnosis, high-dimensional data

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The Power of Ensembles for Active Learning in Image Classification

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