DL0005 Transfer Learning

Transfer learning can leverage knowledge from a pre-trained model to improve performance, reduce training time and data requirements, and lower computational costs when tackling a new but related task.
(1) Leverages Existing Knowledge and Reduced Data Requirements: Transfer learning leverages learned useful representations from large datasets, which can achieve good performance with significantly less task-specific data.
(2) Faster Convergence and Training Time: Starting with pre-trained weights provides a much better initialization point for training than random weights, leading to faster convergence and potentially better local optima. Pre-trained weights have already learned generalizable features, so fine-tuning on a new task typically requires much less training time.
(3) Improved Performance on Limited Data Tasks: When data is limited, transfer learning often yields higher accuracy and better generalization compared to training a model from scratch.