Notes on the Behavior of MC Dropout

6 Aug 2020Francesco VerdojaVille Kyrki

Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness. However the quality of the uncertainty estimated through this method varies and choices in architecture design and in training procedures have to be carefully considered and tested to obtain satisfactory results... (read more)

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