Do deep generative models know what they don't know?
A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. G...
| Main Authors: | , , , , |
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| Format: | Conference item |
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2019
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