how to handle "unknow class" in deep learning model
In the DOG-CAT binary classification problem you define your input data space from a subset of the superset of animals. Thus, the input is meant to come from the DOG-CAT distribution or else it would be out of the scope of this classifier. It would be like you have a classifier to define an expensive or cheap house from an image and you use as input a car. So, you have to redefine what is the problem you want to solve, have a dog-cat, dog-cat-other or dog-cat-frog-etc. classifier. The reduced accuracy is something to be expected because there are probably way many more features that should me extracted in order to classify correctly on these 3 categories. For example, you can tell the difference of a dog from a cat just from the shape of the eyes, but how can you tell the difference of a cat and a small tiger? Using more data probably will lead to better results.
maybe this is a research-based question, but I am posting it here if I got any practical solution.
(OPEN_WORLD_CLASSIFICATION)
problem is simple, let me explain with an example.
I have DOG-CAT classification model. now if let's say I pass "elephant", it will predict as DOG which is wrong.
how to handle this problem, I want to know whenever "unknown" images come to the model.
The approach I tried-: I trained with three class["dog", "cat", "unknown"]. in the "unknown" class folder I put random images.
but the problem with this approach is, it will reduce the overall accuracy of the model.
is there any example for this kind of problem would be appreciated.
Thanks in advance.