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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On MS-COCO last active learning cycle, our method outperforms the semi-supervised method, multi-model methods, and the best AL method by 1.6%.
“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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Sadafi, A., Koehler, N., Makhro, A., Bogdanova, A. Y., Navab, N., Marr, C., & Peng, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_76 · Cited by 38 · see citing works
Hu, Y., Wang, Y., Zhao, T., & Phoon, K. (2020) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0001059 · Cited by 38 · see citing works
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
Hassan, T., Hassan, B., Akram, M. U., Hashmi, S. K., Taguri, A. H., & Werghi, N. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3122172 · Cited by 36 · see citing works
On Statistical Bias In Active Learning: How and When To Fix ItPreprint
Farquhar, S., Gal, Y., & Rainforth, T. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2101.11665 · Cited by 106 · see citing works
Self-citationReal-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
Shen, Y., Zhang, Z., Sabuncu, M. R., & Sun, L. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00075 · Cited by 36 · see citing works
Confidence Calibration for Convolutional Neural Networks Using Structured DropoutPreprint
Zhang, Z., Dalca, A. V., & Sabuncu, M. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09551 · Cited by 36 · see citing works
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Özdemir, F., Peng, Z., Tanner, C., Fürnstahl, P., & Göksel, O. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_21 · Cited by 35 · see citing works
Deep Active Learning with Adaptive Acquisition
Haußmann, M., Hamprecht, F. A., & Kandemir, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/343 · Cited by 38 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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