Definition
Monte Carlo dropout is a technique that uses Dropout at inference time to estimate model uncertainty. The basic idea is to perform multiple forward passes through the network with dropout enabled, each time getting slightly different predictions. These predictions can then be used to estimate the model’s uncertainty.