In cats vs. dogs, we train a model to detect whether a picture has a cat in it, or a dog in it.
What do we do to train a model to detect whether or not there is a cat in the picture at all?
One approach I thought of is to still have two categories: cat and notcat. The cat category will have images of cats, and the notcat category will have images of anything but cats.
I see this as problematic, because you would have to be careful with the data. For example, if notcats didn’t have any animal pictures in it at all, then you would in effect be training a model to predict between animal and notanimal, right?
What’s the correct approach in this situation?