Pre-training: A model is trained on a large, general dataset related to the domain of interest. Fine-tuning: The pre-trained model is then fine-tuned using the smaller, specific dataset from the target task. Transfer: The knowledge from the pre-trained model is transferred to the target task, improving its performance.
