How does transfer learning benefit machine learning?

Transfer learning benefits machine learning by leveraging knowledge gained from previously trained models to enhance performance on new, often related tasks. This approach is particularly advantageous when the amount of labeled data for a new task is limited, as it allows the model to apply insights and patterns learned from a larger dataset.

For example, a neural network trained on a vast image dataset can be fine-tuned on a smaller dataset specific to a niche application, such as medical imaging. Instead of starting the training process from scratch, the model utilizes the features it has already learned, thus accelerating the learning process and improving accuracy.

The other choices highlight aspects that lack the essential advantages of transfer learning. Requiring extensive amounts of labeled data contradicts the purpose of transfer learning, which seeks to reduce that dependency. Creating standalone models for unrelated tasks does not leverage existing knowledge, undermining the efficiency that transfer learning provides. Additionally, the notion that all models must share the same architecture is not inherent to transfer learning, as flexibility in architecture can exist while still utilizing pre-trained models.