This works if you get nicely balanced probabilities out of neural network. Softmax which is popular way to transform network outputs to probabilities pushes pr

This works if you get nicely balanced probabilities out of neural network. Softmax which is popular way to transform network outputs to probabilities pushes probabilities either to zero or one. There may be other ways to transform outputs to probabilities where your approach could work but I'm not aware of them. Also to reply to OP's comments, to have a class like 'I don't know' you need to have a dataset of things that don't look like your training dataset. And you'll always miss examples of things that don't belong to your input class. Bayesian solves it in a nice way by not requiring this infinite not-input training data.