I have a multi-label classification problem wherein each example can belong to one of the pre-defined classes (or can belong to none of them).
I was wondering if I can somehow apply multi-task learning (MTL) to this problem. Essentially, treat each class as a separate class and use a NN based model with common feature extractor layer, and on top of it class specific layer.
My doubt is that generally there is a correlation between labels in a multi-label setting. In the MTL model, after feature extraction each class is handled separately without sharing information. Does a MTL kind of model for multi-label makes sense, given this information?