Which approach is commonly used to increase model efficiency in machine learning tasks?

Transfer learning is an approach where a pre-trained model is adapted for a new but related task, significantly increasing model efficiency. This is particularly valuable when there is a limited amount of labeled data for the new task but a significant amount of data available for a different related task. By utilizing the knowledge gained from the pre-trained model, transfer learning enables models to achieve high performance faster and with fewer resources, thereby reducing the training time and computational cost.

In contrast, data normalization is primarily focused on preparing the data for training rather than enhancing model efficiency. Active learning involves a selective data labeling strategy to improve performance on specific tasks but does not inherently increase the computational efficiency of model training. Batch processing refers to the way data is fed into the model during training, which can improve the speed of execution but does not fundamentally enhance the efficiency of the model itself in the same way transfer learning does.