When is it appropriate to include a “background” or “other” class in a model?
I’m working on finding animals in aerial survey photographs, using single-label and multiple-label classification. There are animals in fewer than 1% of the photographs and the background is extremely variable, including areas of forest, sparse desert vegetation, nearly pure sand, rock, winding rivers, human habitation, agriculture, and so on. Like finding cancer cells, the challenge is finding the needle in the haystack.
Including a ‘background’ class for all non-animal images seems to create problems, because the model achieves the same reduction in loss when it correctly identifies a patch of sand as ‘other’, as when it correctly identifies an animal. I don’t actually want the model to learn anything at all from the very variable background images.
I’m wondering whether it would be better to cull the training images and only train on a smaller set of ‘positives’, i.e., photographs with animals in them. But if I do that, then how can the model indicate a low probability of animals if I gave it a photo with no animals? Is that just a matter of choosing an appropriate loss function?