What uncertainties do we need in Bayesian deep learning for computer vision?
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model – uncertainty which can be explained away given enough data. Traditionally it has been difficult t...
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| Format: | Conference item |
| Language: | English |
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Curran Associates
2017
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