Cited by 392Machine Learning and AlgorithmsAdvanced Bandit Algorithms ResearchGaussian Processes and Bayesian Inference

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Deep Bayesian Active Learning with Image Data

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Ghahramani, Z, Gal, Y, Islam, R·2017·PMLR

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Abstract

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).

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Deep Active Learning with Adaptive Acquisition
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Authors: Ghahramani, Z., Gal, Y., & Islam, R.
Affiliations: McGill University (CA)
University of Cambridge (GB)
Venue: PMLR
Publisher: PMLR
Published: 2017
Pages: 1183-1192
DOI: 10.17863/cam.11070
OpenAlex: W2597787948
Cited by: 392
References: 0
Citations by year: 2025: 4 · 2024: 4 · 2023: 15 · 2022: 22 · 2021: 150 · 2020: 98 · 2019: 73 · 2018: 21 · 2017: 5
FWCI: 24.57
Type: conference paper
Language: en
Open access: green
www.repository.cam.ac.uk
Licence: other-oa
Locations: Apollo (University of Cambridge) (submittedVersion, other-oa)
Cambridge University Engineering Department Publications Database (submittedVersion)
Apollo
International Conference on Machine Learning
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords: active learning (machine learning), Bayesian deep learning, Bayesian convolutional neural network, model uncertainty, acquisition functions, image classification, high-dimensional data, skin cancer diagnosis, MNIST

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Deep Bayesian Active Learning with Image Data

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