What is the primary goal of transfer learning?

The primary goal of transfer learning is to use a model that has been trained on one specific task and apply it to a different but related task. This approach leverages the knowledge gained from the initial training, which can significantly enhance performance on the new task, especially when there is limited data available for the new task.

Transfer learning is particularly beneficial in fields such as natural language processing and computer vision, where foundational models can capture essential features and representations that are relevant across various applications. By doing so, it allows for faster training times and reduced resource utilization, as the core model has already learned valuable patterns from the original dataset.

While the other options mentioned are relevant concepts in machine learning, they do not encapsulate the essence of transfer learning. Creating a new model from scratch does not utilize prior knowledge, and while reducing the amount of training data needed can be a beneficial side effect of transfer learning, it is not its primary purpose. Additionally, data normalization techniques are useful for preparing data but are not directly related to the transfer of learning between tasks.