I was wondering how would it be possible to train a negative class in an image classifier? By negative class (not sure which is the official name of this), I mean a class that denies the presence of the other class(es).
For instance, an important scenario was showcased in the Silicon Valley series with the “Not a Hotdog” app, which would indicate whether the provided image contained a hotdog or not.
So I wonder, how could the “not hotdog” class be trained? I’ve thought that maybe it could be trained with random images of anything, but not sure if that makes any sense.
Also, how could this be trained in a multiclass classifier? To provide some context, we could think of the teddy/grizzly/black bear problem exposed in lesson 2. Should we add a “no bear” class to the classifier? Or would it be better to pipeline 2 models (i.e., 1: bear vs no bear. 2: teddy/grizzly/black).