Interesting suggestion, honestly I'm not sure. Although this should be easy to test empirically. I feel if this were to work, it may depend on architecture of t

Interesting suggestion, honestly I'm not sure. Although this should be easy to test empirically. I feel if this were to work, it may depend on architecture of the network and what intermediate functions are used (ReLu, sigmoid and tanh). But your suggestion is easy to test empirically and I'll try it. There was another method proposed recently for approximating uncertainties: using dropout during evaluation (which effectively makes different runs on the same image as samples). But I came across a discussion on the efficacy of the technique and I wasn't sure. https://www.reddit.com/r/MachineLearning/comments/7bm4b2/d_what_is_the_current_state_of_dropout_as/ https://www.reddit.com/r/MachineLearning/comments/7bm4b2/d_w... My next project is to implement dropout and see how it compares with the current approach. I'll also try your suggestion.