Consider a image labeling problem, where I need to assign one or more labels to an image. The possible labels are
indoor. Human means there is a human in the picture, moving could mean whether the human is runing/walking etc, and indoor classifies whether the image is indoor or outdoor.
Now I can train a DNN with 3 nodes in the output layer using labeled training data. (say node 1, is for +/- human, node 2 +/- moving and so on)
I always thought that this is just another way of doing multi-class classification using DNNs. (Another alternative for multi-class training would have been to train many DNNs with one output node in a pairwise or 1-vs-all fashion.)
But it seems like what I described (training a DNN with 3 output nodes) is Multi-task learning. ( I read wiki on MTL and looked at these papers)
My question is: is the scenario I described MTL? If not what am I missing? If yes, is there anything more to MTL? (clearly there is)
Also I notice in MTL a image can have multiple labels, but usually in multiclass an image is assigned only one - is this the difference?