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Transfer Learning(DL)
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Transfer Learning(DL) Quiz Questions
1.
What is the primary goal of transfer learning in deep learning?
A) To transfer knowledge from one domain to another
B) To train models from scratch on each new task
C) To reduce the size of the neural network
D) To improve the learning rate
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?
A) A model that has not been trained on any data
B) A model that has been trained on a specific task and dataset
C) A model that is randomly initialized
D) A model that is fine-tuned on a new task
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?
A) All layers
B) Only the output layer
C) Only the input layer
D) Some intermediate layers
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?
A) It reduces the need for labeled data for the new task.
B) It guarantees better performance than training from scratch.
C) It increases the complexity of the model.
D) It speeds up the training process.
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?
A) Feature extraction transfer learning
B) Fine-tuning transfer learning
C) Domain adaptation transfer learning
D) Model initialization transfer learning
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?
A) Feature extraction transfers all layers, while fine-tuning transfers only the final layer.
B) Feature extraction fine-tunes all layers, while fine-tuning transfers only some layers.
C) Feature extraction transfers all layers, while fine-tuning transfers all layers as well.
D) Feature extraction is not a form of 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?
A) Adapting a model to a different domain by fine-tuning all layers
B) Adapting a model to a different task by transferring all layers
C) Adapting a model to a different domain by transferring only some layers
D) Adapting a model to a different task by transferring only the input layer
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?
A) Fine-tuning all layers of the pre-trained model
B) Freezing all layers of the pre-trained model except the final layer
C) Training a new model from scratch
D) Adding more layers to the pre-trained model
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?
A) A high learning rate
B) A low learning rate
C) The same learning rate as the new layers
D) No learning rate is applied to 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?
A) Feature extraction transfer learning
B) Fine-tuning transfer learning
C) Domain adaptation transfer learning
D) Zero-shot transfer learning
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?
A) A transfer learning method that requires zero labeled data for the target task
B) A method that transfers all layers of a pre-trained model to a new task
C) A method that trains a new model from scratch for a target task
D) A method that uses a fixed learning rate for pre-trained layers
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?
A) Feature extraction transfer learning
B) Fine-tuning transfer learning
C) Domain adaptation transfer learning
D) Model initialization transfer learning
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?
A) Transferring all layers of the pre-trained model
B) Adapting to a completely different task
C) Addressing the domain shift between source and target domains
D) Ensuring the model performs perfectly on the source domain
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?
A) The domain where the model is first trained
B) The domain where the model is deployed
C) The domain with the smallest dataset
D) The domain with the most diverse data
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Model initialization transfer learning
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?
A) It guarantees better performance than training from scratch.
B) It allows you to skip the data preprocessing step.
C) It reduces the need for hyperparameter tuning.
D) It saves computational resources.
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?
A) It requires a large amount of labeled data for the target task.
B) It can only be applied to image-related tasks.
C) It may not perform as well as other transfer learning methods.
D) It is computationally expensive.
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Model initialization transfer learning
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?
A) To transfer all layers of a pre-trained model
B) To adapt a model to a completely different task
C) To handle the differences between source and target domains
D) To improve the performance on the source domain
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Zero-shot transfer learning
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?
A) It requires no additional training.
B) It adapts the model to a new task with minimal changes.
C) It transfers only the input layer.
D) It guarantees superior performance.
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?
A) The domain where the model is first trained
B) The domain with the largest dataset
C) The domain where the model is deployed
D) The domain to which the model is adapted
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?
A) It fine-tunes all layers of the pre-trained model.
B) It requires no changes to the pre-trained model.
C) It transfers all layers of the pre-trained model.
D) It guarantees better performance than fine-tuning.
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Model initialization transfer learning
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?
A) It speeds up the training process.
B) It allows for fine-tuning of all layers.
C) It guarantees better performance.
D) It simplifies the model architecture.
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Zero-shot transfer learning
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?
A) Adapting the model to the source domain
B) Handling the domain shift between source and target domains
C) Finding a suitable pre-trained model
D) Choosing the right learning rate
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Zero-shot transfer learning
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?
A) To transfer knowledge from one model to another
B) To train a new model from scratch
C) To adapt a model to a different domain
D) To fine-tune all layers of a pre-trained model
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?
A) Fine-tuning transfer learning
B) Feature extraction transfer learning
C) Domain adaptation transfer learning
D) Zero-shot transfer learning
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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