Fine-tuning (machine learning) in the context of Deep learning

Fine-tuning (in deep learning) is the process of adapting a model trained for one task (the upstream task) to perform a different, usually more specific, task (the downstream task). It is considered a form of transfer learning, as it reuses knowledge learned from the original training objective.

Fine-tuning involves applying additional training (e.g., on new data) to the parameters of a neural network that have been pre-trained. Many variants exist. The additional training can be applied to the entire neural network, or to only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). A model may also be augmented with "adapters"—lightweight modules inserted into the model's architecture that nudge the embedding space for domain adaptation. These contain far fewer parameters than the original model and can be fine-tuned in a parameter-efficient way by tuning only their weights and leaving the rest of the model's weights frozen.