A pre-trained model is a neural network that has been trained on a relevant general dataset.
This model can then be used for specialized use-cases after going through a transfer learning process.
Transfer learning
Transfer learning is the process of specializing a pre-trained model for a certain use-case.
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For example, ResNet is a pre-trained model for computer vision. You can use ResNet to train models for use-cases such as classifying dogs and cats or curly and straight hair.
This is used for efficiency: it speeds up the training and requires a smaller data set.
An analogy: imagine you need to teach a chess opening. Would it be easier to teach which person:
- One that already knows Chess: has already played before, knows how pieces moves, knows the ultimate goal
- One that has never seen Chess before
In this analogy, the former is a pre-trained model and the latter is training from scratch.