Feature-based transfer learning involves leveraging certain features learned by a pre-trained model

and adapting them to a new task. Here’s a detailed explanation:

Feature Selection: This process involves identifying and selecting specific features or layers from a

pre-trained model that are relevant to the new task while discarding others that are not.

Adaptation: The selected features are then fine-tuned or re-trained on the new dataset, allowing the

model to adapt to the new task with improved performance.

Efficiency: This approach is computationally efficient because it reuses existing features, reducing the

amount of data and time needed for training compared to starting from scratch.

Reference:

Pan,

S. J., & Yang,

Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data

Engineering, 22(10), 1345-1359.

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural

Networks? In Advances in Neural Information Processing Systems.