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I am pretty new to Python and this board so I am not sure, if I am at the right place for my question since it doesn't include any code. If not so, please give my a hint for a better way/place to ask.

I am struggling with using Monte Carlo Dropouts for determine Uncertainty for my image classificator using ResNet18.

I have read several papers to this topic and I am still kinda confused about this topic.

I know already to use dropout like

def dropout2d(input, p=0.5, training=True, inplace=False):

multiply times for getting a variance which can be interpreted as the uncertainty. I am pretty sure, that I already understood how MC-Dropout works in general.

So coming to my questions:

Do I use MC-dropout while training or testing and why? I feel like I have read different ways.

I have searched for a coding example from the dropouts but didn't find anything. Do you know Paper/ example code/ searchlink for a proper code?

Is

nn.dropout2d(imput, p=x, ...) the right way to use dropout for a image classificator?

Thanks for your help.

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  • $\begingroup$ Just to say that your question is perfectly fine here, but I don't know the answer sorry $\endgroup$ – Erwan Jun 27 '19 at 11:23
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Monte Carlo Dropouts (MCDO) is used during the prediction / inference phase to provide an estimate of uncertainty for the model's predictions.

Regular dropout during the training phase is a regularization technique.

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  • $\begingroup$ For the case of regression, would this correspond to confidence intervals around the conditional expectation or characterize the full conditional distribution of the target? $\endgroup$ – Akylas Stratigakos Jan 4 at 12:27

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