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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 ...


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Feature extraction (FE) is not the same as representation learning (RL), but they are similar and related. You describe accurately what feature extraction typically refers to, i.e. the process of extracting (new) features from existing ones or raw data (e.g. images). For example, let's say you have a dataset associated with a car. You have only two features ...


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