Transfer Learning(DL) QUIZ (MCQ QUESTIONS AND ANSWERS)

It guarantees better performance than training from scratch.

It allows you to skip the data preprocessing step.

It reduces the need for hyperparameter tuning.

It saves computational resources.

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

To transfer knowledge from one model to another

To train a new model from scratch

To adapt a model to a different domain

To fine-tune all layers of a pre-trained model

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Adapting the model to the source domain

Handling the domain shift between source and target domains

Finding a suitable pre-trained model

Choosing the right learning rate

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

It speeds up the training process.

It allows for fine-tuning of all layers.

It guarantees better performance.

It simplifies the model architecture.

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

It fine-tunes all layers of the pre-trained model.

It requires no changes to the pre-trained model.

It transfers all layers of the pre-trained model.

It guarantees better performance than fine-tuning.

The domain where the model is first trained

The domain with the largest dataset

The domain where the model is deployed

The domain to which the model is adapted

It requires no additional training.

It adapts the model to a new task with minimal changes.

It transfers only the input layer.

It guarantees superior performance.

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

To transfer all layers of a pre-trained model

To adapt a model to a completely different task

To handle the differences between source and target domains

To improve the performance on the source domain

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

It requires a large amount of labeled data for the target task.

It can only be applied to image-related tasks.

It may not perform as well as other transfer learning methods.

It is computationally expensive.

To transfer knowledge from one domain to another

To train models from scratch on each new task

To reduce the size of the neural network

To improve the learning rate

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

The domain where the model is first trained

The domain where the model is deployed

The domain with the smallest dataset

The domain with the most diverse data

Transferring all layers of the pre-trained model

Adapting to a completely different task

Addressing the domain shift between source and target domains

Ensuring the model performs perfectly on the source domain

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

A transfer learning method that requires zero labeled data for the target task

A method that transfers all layers of a pre-trained model to a new task

A method that trains a new model from scratch for a target task

A method that uses a fixed learning rate for pre-trained layers

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

A high learning rate

A low learning rate

The same learning rate as the new layers

No learning rate is applied to the pre-trained layers

Fine-tuning all layers of the pre-trained model

Freezing all layers of the pre-trained model except the final layer

Training a new model from scratch

Adding more layers to the pre-trained model

Adapting a model to a different domain by fine-tuning all layers

Adapting a model to a different task by transferring all layers

Adapting a model to a different domain by transferring only some layers

Adapting a model to a different task by transferring only the input layer

Feature extraction transfers all layers, while fine-tuning transfers only the final layer.

Feature extraction fine-tunes all layers, while fine-tuning transfers only some layers.

Feature extraction transfers all layers, while fine-tuning transfers all layers as well.

Feature extraction is not a form of transfer learning.

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

It reduces the need for labeled data for the new task.

It guarantees better performance than training from scratch.

It increases the complexity of the model.

It speeds up the training process.

All layers

Only the output layer

Only the input layer

Some intermediate layers

A model that has not been trained on any data

A model that has been trained on a specific task and dataset

A model that is randomly initialized

A model that is fine-tuned on a new task