Hi Thomas, thanks for your quick reply.
It’s a great idea to group together exclusive options, I have actually done some testing with that and it’s not that difficult to implement. Essentially, it means the network becomes a multi-task model, with each exclusive label group being a task. Each image trains only 1 task at a time, while ignoring the other outputs.
A new problem shows up with this approach however:
Some images simply do not belong to any of the labels within an exclusive group. A color should always be predicted, an object simply has to be long to one color. That is a good thing, because the network is trained to do always predict one color. However, let’s say I have an image of a bike and I have a group of exclusive labels called [4-doors, 2-doors, 1-door], indicating how many car doors there are in the picture. The image of a bike simply doesn’t belong to ANY of the exclusive labels, but the network will probably guess the most-common label anyway, since those outputs of that exclusive group have been trained to always predict one.
Do you see the problem I am trying to describe here? Perhaps there is always a way to structure the exclusive label groups so that there is never a group that can have zero labels predicted. Or do you have a suggestion on how to deal with this problem?