Should one use an "other" category in image classification? In image classification, there are sometimes images that do not fit in any category.
For example, if I build a CNN in Keras to classify Dogs and Cats, does it help (in terms of training time and performance) to create an &quot;other&quot; (or &quot;unclassified&quot;) category in which images of houses, people, birds, etc., are classified? Is there any research paper that discusses this?
A similar question was asked before here, but, unfortunately, it has no answer.
It is not advisable because if you use an &quot;other&quot; class, you are just increasing problems for your network. Since &quot;other&quot; means not dog and not cat, then, what common feature does it have? Most of the time the &quot;other&quot; images won't have many features in common. If they do, then go ahead and make an &quot;other&quot; class.
There is a better way: if the probabilities for both cat and dog are less than a threshold (you need to decide that, take, 0.5), then, you can say it is an &quot;other&quot; object.