What is the difference between multi-label and multi-task classification?

Question

I am working on a data-set that has multiple labels associated with it (not necessarily independent of each other). During my development, I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach. Is there any fundamental difference between the two?

Answer 1

Is there any fundamental difference between the two?

The difference is in the names:

• Multi task means that we are learning more than a single task, i.e. the labels we have will be used to compute different losses
• Multi label means that for a single task, more than one label is allowed as correct prediction. but in practice, the different labels will be used to compute a single loss.

You can also visualize the difference: a network trained on multi tasks will have more output layers, one per task,whereas a network trained on multi label classification require only one output layer. Note that in a multi task learning setting one of the tasks could very well be multi label classification.

I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach

That depends on what you're trying to do, and we can't even give suggestions without knowing the data.