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Deep Bayesian Active Learning with Image Data
Open accessAbstract
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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“We observe that this difference is maintained in the other cycles, including the last active learning cycle where our method outperforms the semisupervised method, multi-model methods [9, 12] and the best AL method [5] by 1 . 6% .”
From Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection (2021) · cites this paper as [12]
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
“DBAL [101], i.”
A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10562-9 · Cited by 1,670
The Power of Ensembles for Active Learning in Image Classification
Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00976 · Cited by 655
Learning Loss for Active Learning
Yoo, D., & Kweon, I. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00018 · Cited by 740
Nair, T., Precup, D., Arnold, D. L., & Arbel, T. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101557 · Cited by 451
Metadata
- Authors
- Ghahramani, Z., Gal, Y., & Islam, R.
- Affiliations
- McGill University (CA)
- University of Cambridge (GB)
- Venue
- PMLR
- Publisher
- PMLR
- Published
- 2017
- Pages
- 1183-1192
- 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
- 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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