<p>I agree with you that this is a key question, but I am not aware of much work in that area either. </p>

<p>There's one recent paper by <a href="https://arxiv.org/abs/1511.03719" rel="noreferrer">Zhang and LeCun</a>, that addresses the question for image classification in particular. They use large quantities of unlabelled data to create an additional "none of the above" class. The catch though is that, in some cases, their unlabelled data is not completely unlabelled, and they have means of removing "unlabelled" images that are actually in one of their labelled classes. Having said that, the authors report that apart from solving the "none of the above" problem, they even see performance gains even on their test sets.</p>

<p>As for fitting something post-hoc, just by looking at the outputs of the softmax, I can't provide any pointers.</p>
How best to deal with "None of the above" in Image Classification?
<p>This seems to be a fundamental question which some of you out there must have an opinion on.  I have an image classifier implemented in CNTK with 48 classes.  If the image does not match any of the 48 classes very well, then I'd like to be able to conclude that it was not among these 48 image types.  My original idea was simply that if the highest output of the final Softmax layer was low, I would be able to conclude that the test image matched none well.  While I occasionally see this occur, in most testing, Softmax still produces a very high (and mistaken) result when handed an 'unknown image type'.  But maybe my network is 'over fit' and if it wasn't, my original idea would work fine.  What do you think?  Any way to define a 49-th class called 'none-of-the-above'?</p>