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Question 27 - D-GAI-F-01 discussion

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What is feature-based transfer learning?

A.
Transferring the learning process to a new model
Answers
A.
Transferring the learning process to a new model
B.
Training a model on entirely new features
Answers
B.
Training a model on entirely new features
C.
Enhancing the model's features with real-time data
Answers
C.
Enhancing the model's features with real-time data
D.
Selecting specific features of a model to keep while removing others
Answers
D.
Selecting specific features of a model to keep while removing others
Suggested answer: D

Explanation:

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.

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.

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asked 16/09/2024
Ellee Chen
40 questions
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