Deep Bayesian active learning with image data
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 fr...
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| Format: | Conference item |
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PMLR
2017
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| _version_ | 1826303579198062592 |
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| author | Gal, Y Islam, R Ghahramani, Z |
| author_facet | Gal, Y Islam, R Ghahramani, Z |
| author_sort | Gal, Y |
| collection | OXFORD |
| description | 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). |
| first_indexed | 2024-03-07T06:04:51Z |
| format | Conference item |
| id | oxford-uuid:ed7a67de-5cfd-4d0d-ae27-6c3221a8dff6 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:04:51Z |
| publishDate | 2017 |
| publisher | PMLR |
| record_format | dspace |
| spelling | oxford-uuid:ed7a67de-5cfd-4d0d-ae27-6c3221a8dff62022-03-27T11:25:20ZDeep Bayesian active learning with image dataConference itemhttp://purl.org/coar/resource_type/c_5794uuid:ed7a67de-5cfd-4d0d-ae27-6c3221a8dff6Symplectic Elements at OxfordPMLR2017Gal, YIslam, RGhahramani, ZEven 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). |
| spellingShingle | Gal, Y Islam, R Ghahramani, Z Deep Bayesian active learning with image data |
| title | Deep Bayesian active learning with image data |
| title_full | Deep Bayesian active learning with image data |
| title_fullStr | Deep Bayesian active learning with image data |
| title_full_unstemmed | Deep Bayesian active learning with image data |
| title_short | Deep Bayesian active learning with image data |
| title_sort | deep bayesian active learning with image data |
| work_keys_str_mv | AT galy deepbayesianactivelearningwithimagedata AT islamr deepbayesianactivelearningwithimagedata AT ghahramaniz deepbayesianactivelearningwithimagedata |