Fine-tuning should be applied in the model training process when we have a pre-trained model that has been trained on a large dataset and we want to adapt it to a specific task or domain with a smaller labeled dataset. Fine-tuning is especially useful in transfer learning, where the knowledge acquired by a model from one task is utilized to improve performance on a different but related task.

  The general workflow for fine-tuning involves the following steps:

  1. Selecting a pre-trained model: Choose a pre-trained model that has been trained on a large dataset and has shown good performance on a similar task or domain. Popular choices include models like BERT, GPT-2, and ResNet.

  2. Removing the last few layers: In most cases, the last few layers of the pre-trained model, called the "top layers", are task-specific and need to be replaced or retrained. These layers are responsible for making predictions on the original task and need to be adjusted to perform well on the new task.

  3. Adding task-specific layers: Introduce new layers on top of the remaining layers of the pre-trained model. These layers should be tailored to the specific task or domain you want to fine-tune the model for. The number and complexity of these layers depend on the complexity of the new task.

  4. Training the combined model: Train the entire model, including the pre-trained layers and the newly added layers, on the labeled dataset specific to the target task. This is typically done by minimizing a suitable loss function like cross-entropy or mean squared error.

  5. Fine-tuning: Gradually update the weights of the pre-trained layers by backpropagating the gradients computed during training. However, it is important to use a much lower learning rate for these layers compared to the newly added layers. This approach allows the model to preserve its previously learned knowledge while adapting to the new task.

  The decision of when to apply fine-tuning depends on the availability of a pre-trained model suitable for the task, and the size and similarity of the labeled dataset for the new task. If a pre-trained model is not available or the new task has a sufficiently large labeled dataset, it may be more beneficial to train a model from scratch rather than performing fine-tuning.