Figure 4
From: Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels
Visual comparison of aleatoric and epistemic uncertainty estimation techniques. (a) Test-time augmentation randomly samples multiple views of each input. (b) Monte Carlo dropout samples multiple configurations of the model weights by randomly excluding a subset of neurons.
