What is the main benefit of transfer learning in machine learning?

Transfer learning is primarily beneficial because it enables faster model development and improves model performance by leveraging knowledge gained from previously trained models. In traditional machine learning, training a model from scratch often requires large datasets to achieve acceptable performance, which can be time-consuming and resource-intensive. Transfer learning mitigates this issue by allowing a model that has been pretrained on a large dataset to be fine-tuned for a specific task that may have considerably less available data.

By starting with a model that already understands general features, such as shapes or colors from the original dataset, the process is streamlined. It often results in better performance in tasks with limited data and requires less computation because the foundational understanding has already been built into the model. This is why the statement about generating models faster with less data captures the essence of the main benefit of transfer learning in machine learning.