Hello, I am attempting to create an image classification model that can detect when a picture does not fall into any of the categories. It is somewhat similar to the question in Cats or dogs or neither?. The solution proposed is to just train it with pictures of dogs, cats, and non-dog/cats. However, it seems to me that there is a potential problem with this approach. Taking the dogs vs cat vs neither problem:
Assume that the neither category consists mostly of elephants and pigs. The model would simply associate the neither category to an animal that is an elephant-pig hybrid. Since all of the categories consists of 4 legged animals, if I then passed a picture of a fish or tree to this trained model, it does not seem that the model will be able to accurately classify this new image as neither. Is my understanding here correct?
It seems like the above solution would also hinder the training process. For example in training, if a picture of a sheep was encountered, it would be trying to fit the sheep as a cat/dog/elephant-pig rather than cat/dog/neither.
If we set the model to classify any picture with a confidence below a certain threshold as neither, wouldn’t it be easily confused by pictures of wolves or tigers? These animals seem to share more similarity to cats/dogs rather than the ‘neither’ category and would lead to a high confidence of it being classified as a dog or cat rather than neither.
Are such concerns valid? Is there a better solution to this problem?