using a model that has acquired general understanding through self supervision and then you just fine tune it with little label data for the tasks you really want to solve what you gain from that is overall well first of all since your first building up general understanding and then fine-tuning your model to the task you want to solve and that means your model will need less label data to achieve better performance your model will converge faster because its weight already in a somewhat optimal position and also your model is going to