Difference between using BERT as a 'feature extractor' and fine tuning BERT with its layers fixed

Question

I understand that there are two ways of leveraging BERT for some NLP classification task:

1. BERT might perform ‘feature extraction’ and its output is input further to another (classification) model
2. The other way is fine-tuning BERT on some text classification task by adding an output layer or layers to pretrained BERT and retraining the whole (with varying number of BERT layers fixed)

However, if in the second case, we fix all the layers and add ALL the layers from the classification model will be added, 1st and 2nd approaches are effectively the same, am I right?

Answer 1

Indeed. You might want to read this paper comparing the 2 approaches on various NLP tasks: To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

The main conclusion is that results are quite similar in most cases.