Transfer Learning(DL) Quiz Questions

1. What is the primary goal of transfer learning in deep learning?

view answer: A) To transfer knowledge from one domain to another
Explanation: The primary goal of transfer learning is to transfer knowledge from one domain or task to another.
2. In transfer learning, what is a pre-trained model?

view answer: B) A model that has been trained on a specific task and dataset
Explanation: A pre-trained model is a model that has been trained on a specific task and dataset before further adaptation.
3. Which layer(s) of a pre-trained neural network are typically modified or fine-tuned for a new task in transfer learning?

view answer: D) Some intermediate layers
Explanation: In transfer learning, some intermediate layers of a pre-trained network may be fine-tuned for a new task.
4. What is the main advantage of using a pre-trained model for a new task?

view answer: A) It reduces the need for labeled data for the new task.
Explanation: Using a pre-trained model can reduce the need for labeled data for the new task, making it advantageous, especially when data is limited.
5. Which type of transfer learning involves fine-tuning all layers of a pre-trained model for a new task?

view answer: B) Fine-tuning transfer learning
Explanation: Fine-tuning transfer learning involves fine-tuning all layers of a pre-trained model for a new task.
6. What is the primary difference between feature extraction transfer learning and fine-tuning transfer learning?

view answer: A) Feature extraction transfers all layers, while fine-tuning transfers only the final layer.
Explanation: Feature extraction transfer learning transfers all layers except the final layer, while fine-tuning transfer learning can involve fine-tuning some or all layers.
7. In transfer learning, what is domain adaptation?

view answer: C) Adapting a model to a different domain by transferring only some layers
Explanation: Domain adaptation involves adapting a model to a different domain by transferring only some layers.
8. Which technique is commonly used for feature extraction transfer learning?

view answer: B) Freezing all layers of the pre-trained model except the final layer
Explanation: Feature extraction transfer learning typically involves freezing all layers of the pre-trained model except the final layer.
9. In fine-tuning transfer learning, what is the learning rate typically set for the pre-trained layers?

view answer: B) A low learning rate
Explanation: In fine-tuning transfer learning, the learning rate for the pre-trained layers is typically set to a low value.
10. Which type of transfer learning is often used when the source and target domains have some similarities but also differences?

view answer: C) Domain adaptation transfer learning
Explanation: Domain adaptation transfer learning is used when source and target domains have some similarities but also differences.
11. What is zero-shot transfer learning?

view answer: A) A transfer learning method that requires zero labeled data for the target task
Explanation: Zero-shot transfer learning is a method that requires zero labeled data for the target task.
12. Which transfer learning approach is best suited for situations where the source and target tasks are very similar?

view answer: B) Fine-tuning transfer learning
Explanation: Fine-tuning transfer learning is best suited for situations where the source and target tasks are very similar.
13. What is the main challenge in domain adaptation transfer learning?

view answer: C) Addressing the domain shift between source and target domains
Explanation: The main challenge in domain adaptation transfer learning is addressing the domain shift between source and target domains.
14. In transfer learning, what is the source domain?

view answer: A) The domain where the model is first trained
Explanation: The source domain is the domain where the model is first trained.
15. Which type of transfer learning is often used in computer vision for tasks like image classification?

view answer: A) Fine-tuning transfer learning
Explanation: Fine-tuning transfer learning is often used in computer vision for tasks like image classification.
16. What is the primary advantage of using a pre-trained model for transfer learning?

view answer: A) It guarantees better performance than training from scratch.
Explanation: Using a pre-trained model for transfer learning can provide better performance than training from scratch.
17. What is the main limitation of zero-shot transfer learning?

view answer: C) It may not perform as well as other transfer learning methods.
Explanation: The main limitation of zero-shot transfer learning is that it may not perform as well as other transfer learning methods.
18. Which type of transfer learning is often used in natural language processing (NLP) for tasks like text classification?

view answer: A) Fine-tuning transfer learning
Explanation: Fine-tuning transfer learning is often used in NLP for tasks like text classification.
19. What is the primary goal of domain adaptation in transfer learning?

view answer: C) To handle the differences between source and target domains
Explanation: The primary goal of domain adaptation in transfer learning is to handle the differences between source and target domains.
20. Which type of transfer learning is often used when the source and target tasks are completely unrelated?

view answer: D) Zero-shot transfer learning
Explanation: Zero-shot transfer learning is often used when the source and target tasks are completely unrelated.
21. What is the primary advantage of fine-tuning transfer learning?

view answer: B) It adapts the model to a new task with minimal changes.
Explanation: Fine-tuning transfer learning adapts the model to a new task with minimal changes to the pre-trained model.
22. In domain adaptation transfer learning, what is the target domain?

view answer: D) The domain to which the model is adapted
Explanation: The target domain is the domain to which the model is adapted in domain adaptation transfer learning.
23. What is the primary advantage of feature extraction transfer learning?

view answer: B) It requires no changes to the pre-trained model.
Explanation: Feature extraction transfer learning requires no changes to the pre-trained model, making it straightforward to apply.
24. Which type of transfer learning is most suitable for situations where computational resources are limited?

view answer: B) Feature extraction transfer learning
Explanation: Feature extraction transfer learning is suitable for situations with limited computational resources.
25. What is the primary advantage of using a pre-trained model as a feature extractor in feature extraction transfer learning?

view answer: D) It simplifies the model architecture.
Explanation: Using a pre-trained model as a feature extractor simplifies the model architecture in feature extraction transfer learning.
26. Which type of transfer learning is often used when there is a lack of labeled data for the target task?

view answer: D) Zero-shot transfer learning
Explanation: Zero-shot transfer learning is used when there is a lack of labeled data for the target task.
27. What is the primary challenge in zero-shot transfer learning?

view answer: C) Finding a suitable pre-trained model
Explanation: The primary challenge in zero-shot transfer learning is finding a suitable pre-trained model for the target task.
28. Which type of transfer learning is often used when the source and target tasks are related but not identical?

view answer: B) Feature extraction transfer learning
Explanation: Feature extraction transfer learning is often used when the source and target tasks are related but not identical.
29. What is the primary goal of model initialization transfer learning?

view answer: A) To transfer knowledge from one model to another
Explanation: The primary goal of model initialization transfer learning is to transfer knowledge from one model to another.
30. Which type of transfer learning is often used when the source and target tasks are very similar, but there is a lack of labeled data for the target task?

view answer: C) Domain adaptation transfer learning
Explanation: Domain adaptation transfer learning is often used when the source and target tasks are very similar, but labeled data is scarce for the target task.

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