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Monte Carlo dropout may effectively capture model uncertainty in deeplearning, where a measure of uncertainty is obtained by using multipleinstances of dropout at test time. However, Monte Carlo dropout is appliedacross the whole network and thus significantly increases the computationalcomplexity, proportional to the number of instances. To reduce thecomputational complexity, at test time we enable dropout layers only near theoutput of the neural network and reuse the computation from prior layers whilekeeping, if any, other dropout layers disabled. Additionally, we leverage theside information about the ideal distributions for various input samples to do`error correction' on the predictions. We apply these techniques to the radiofrequency (RF) transmitter classification problem and show that the proposedalgorithm is able to provide better prediction uncertainty than the simpleensemble average algorithm and can be used to effectively identify transmittersthat are not in the training data set while correctly classifying transmittersit has been trained on.

Fast Monte Carlo Dropout and Error Correction for Radio Transmitter Classification