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.
* References:
* 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.