I am reading this paper http://zli115.web.engr.illinois.edu/wp-content/uploads/2016/10/0479.pdf
It distinguishes between feature extraction and fine tuning in deep learning. I am not getting the difference as feature extraction is just the same as fine tuning:
As per my understanding:
You train a model on a dataset, use it for training on another dataset. This is fine tuning. This is the same as feature extraction from the first trained model, like in feature extraction also you take the first model and train it on a new dataset.
Is there any difference between the two in the ml literature?
Joint training is a third category I understand as there you train on all data simultaneously.