What is the theoretical differences of Multitask learning vs Fine tuning based transfer learning?
As per my understanding,, in the both scenerio above when learning the object type and color what you are doing is a multitask learning. That is you are teaching your model to do two task simultaneously, 1. Predicting Object type(Which fruit/shape) and 2. Predicing color. So it is not like TL rather Multi task learning.
1.
TL does have some source task.But all you do in TL is that you freeze the whole model except the last few layers and retrain them on your specific dataset.(or add fews layers at the end and train them). TL doesn't train your model from beginning fro new task. But the aboe scenerios are not TL. So it is ambiguous to name them source and target task.
2.As said above, both of them are multitask learning rather than TL.So t is ambiguous to name them source and target task.
3.Both of the scenerio mentioned is multitask learning. Although you can use any of them as your source task and do a TL on it to work on the other. For example, you can train your Fruits and Color model in scenerio 1 and do a transfer learning on it for the second scenerio(to learn shape and color). As they have some same properties underneath, so transfer learning is a valid option here.