Network architecture. After the flatten layer, network I branch out into a dense network per parameter, resulting in five unique network arms, which allow for parameter-specific learning. The next time the arms branch out into a network prediction and an aleatoric uncertainty prediction. To capture the epistemic error of network I, we make N predictions on the same image to sample the network posterior. Both networks have ReLU activation functions unless stated otherwise.