Question

Which technique forces a model to perform multiple stochastic forward passes during inference to estimate the variance in its predictions as a measure of uncertainty?

Accepted Answer

The technique is called Monte Carlo Dropout. In a standard neural network, dropout is a process used during training where random neurons are deactivated to prevent the model from relying too heavily on any single input, which helps avoid overfitting. Monte Carlo Dropout applies this same concept during inference, which is the stage where the model makes predictions on new data. By keeping dropout layers active during inference, the model produces a different output every time it processes the same input because different sets of neurons are randomly deactivated in each pass. By performing multiple stochastic forward passes—meaning multiple runs that each include a different random selection of deactivated neurons—the model generates a distribution of possible outputs. Calculating the variance of these outputs provides a measure of epistemic uncertainty, which represents the model&#x27;s lack of knowledge or confidence regarding specific input data. If the outputs across the different passes are highly varied, the model is considered uncertain, whereas consistent outputs indicate higher confidence.

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Which technique forces a model to perform multiple stochastic forward passes during inference to estimate the variance in its predictions as a measure of uncertainty?