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Typically, in transfer learning, you have two stages/steps (as you realized) pre-train some base model $M_\text{base}$ (i.e. the feature extraction part, where this pre-trained model is supposed to learn representations of the data, which can later be exploited to solve another task) on some "general" dataset $A$; note that you may not necessarily ...