Hi Sam!



 shartzog:

Prior to now, I’ve been thinking about my multi-label model as if it were a set of parallel binary classifiers, but that’s clearly a bad analogy.


Just one quick comment:  On the contrary, I think understanding a
multi-label, multi-class classifier as a set of parallel binary classifiers
is exactly the right way to think about it.  The “parallel” classifiers
have an efficiency advantage in training and inference, because
you pass any given sample through one network instead of many,
and much of the work is shared.  They can also have an accuracy
advantage, because training on all of the classes at the same time
can help your classifier learn more “insightful” features that end up
working better for all classes.  (One could certainly imagine, however,
a ten-class dataset, where the first nine classes cause training on
all ten classes to hurt the performance for the tenth class.)
Best.
K. Frank