<i>Isn&#x27;t &quot;confidence&quot; measuring the internal state of the net</i><p>I don&#x27;t quite follow your meaning but no, that&#x27;s not quite how I think about it.<p>If x is the input image and C is the class selected by the classifier we expect the confidence to be representative of the conditional probability P( C | x ).  There is theory that shows this to be true given certain assumptions, but as is very often the case in this field the extent to which those assumptions are valid is rarely clear.  So if the net outputs a confidence of 0.9999 we expect there to be only 1 chance in 10000 that the true class is something other than C.  If we test the net and find such a confidence associated with an incorrect classification more frequently than predicted then we can say that the confidence is objectively incorrect.