Does fine-tuning of transferred layers perform better than frozen transferred layers?
Transfer learning means to apply the knowledge that some machine learning model holds (represented by its learned parameters) to a new (but in some way related) task. Whereas fine-tuning means taking some machine learning model that has already learned something before (i.e. been trained on some data) and then training that model (i.e. training it some more, possibly on different data).

From this we can conclude that if we are to use the learning of one model and concentrate only on some specific part of it, we can use transfer learning and train the network again with only a little amount of data. Whereas if we have sufficient data that we want the model to be trained upon, fine-tuning the model will come to the rescue. Refer to the previous question and this article