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

Computer vision tasks such as object recognition and image classification

Natural language processing tasks such as sentiment analysis and machine translation

Speech recognition tasks

All of the above

Transfer learning involves reusing knowledge from a source task to improve performance on a target task, while data augmentation involves creating new training examples from existing ones.

Transfer learning involves creating new training examples from existing ones, while data augmentation involves reusing knowledge from a source task to improve performance on a target task.

Transfer learning and data augmentation are the same thing.

None of the above.

Transfer learning involves reusing knowledge from a source task to improve performance on a target task, while ensemble learning involves combining multiple models to improve performance on a task.

Transfer learning involves combining multiple models to improve performance on a task, while ensemble learning involves reusing knowledge from a source task to improve performance on a target task.

Transfer learning and ensemble learning are the same thing.

None of the above.

Transfer learning involves reusing knowledge from a source task to improve performance on a target task, while meta-learning involves learning how to learn from multiple tasks.

Transfer learning involves learning how to learn from multiple tasks, while meta-learning involves reusing knowledge from a source task to improve performance on a target task.

Transfer learning and meta-learning are the same thing.

None of the above.

Horizontal transfer involves transferring knowledge between similar tasks, while vertical transfer involves transferring knowledge between different levels of a task hierarchy.

Horizontal transfer involves transferring knowledge between different levels of a task hierarchy, while vertical transfer involves transferring knowledge between similar tasks.

Horizontal transfer and vertical transfer are the same thing.

None of the above.

Feature extraction involves using the pre-trained model for feature extraction and training a new model on top of the extracted features, while fine-tuning involves adding new layers to the pre-trained model and training the entire model on a new task.

Feature extraction involves adding new layers to the pre-trained model and training the entire model on a new task, while fine-tuning involves using the pre-trained model for feature extraction and training a new model on top of the extracted features.

Feature extraction involves using the pre-trained model as-is for a new task, while fine-tuning involves retraining the entire model from scratch on a new task.

None of the above.

Supervised transfer learning involves transferring knowledge from a labeled source task to a labeled target task, while unsupervised transfer learning involves transferring knowledge from an unlabeled source task to an unlabeled target task.

Supervised transfer learning involves transferring knowledge from an unlabeled source task to a labeled target task, while unsupervised transfer learning involves transferring knowledge from a labeled source task to an unlabeled target task.

Supervised transfer learning and unsupervised transfer learning are the same thing.

None of the above.

Transfer learning involves learning from a single task and applying it to a new task, while multi-task learning involves learning from multiple tasks simultaneously.

Transfer learning involves learning from multiple tasks simultaneously, while multi-task learning involves learning from a single task and applying it to a new task.

Transfer learning and multi-task learning are the same thing.

None of the above.

Transfer learning involves reusing knowledge from a source task to improve performance on a target task, while domain adaptation involves adapting a model trained on one domain to a new domain.

Transfer learning involves adapting a model trained on one domain to a new domain, while domain adaptation involves reusing knowledge from a source task to improve performance on a target task.

Transfer learning and domain adaptation are the same thing.

None of the above.

The lack of transferability between tasks

The need for expertise in both the source and target tasks

The potential for negative transfer, where knowledge from the source task hurts performance on the target task

All of the above

Improved model performance

Reduced training time and computational resources

Reduced need for labeled data

All of the above

The process of transferring knowledge between multiple modalities

The process of transferring knowledge between multiple domains

The process of transferring knowledge between multiple tasks

None of the above

The process of transferring knowledge between different types of recommender systems

The process of transferring knowledge between different domains in recommender systems

The process of transferring knowledge between different users in recommender systems

None of the above

The process of transferring knowledge between different reinforcement learning agents

The process of transferring knowledge between different domains in reinforcement learning

The process of transferring knowledge between different tasks in reinforcement learning

None of the above

Learning multiple tasks simultaneously

Learning from multiple sources simultaneously

Learning from multiple domains simultaneously

None of the above

Learning from a single example

Learning from a small number of examples

Learning from a large number of examples

Learning without any labeled examples from the target domain

Learning from a single example

Learning from a small number of examples

Learning from a large number of examples

None of the above

Learning from a single example

Learning from a small number of examples

Learning from a large number of examples

None of the above

To increase the model's capacity to learn complex patterns

To speed up the training process by leveraging pre-trained models

To improve the model's performance on noisy data

To reduce the model's memory requirements

The lack of transferability between tasks

The availability of large amounts of labeled data for the source task

The need for expertise in both the source and target tasks

None of the above

The process of adapting a model trained on one domain to a new domain

The process of adapting a model trained on one task to a new task

The process of adapting a model trained on one language to a new language

The process of adapting a model trained on one dataset to a new dataset

LSTM, GRU, Transformer

VGG, Inception, ResNet

PCA, LDA, ICA

K-Means, DBSCAN, Hierarchical clustering

VGG, Inception, ResNet

LSTM, GRU, Transformer

PCA, LDA, ICA

K-Means, DBSCAN, Hierarchical clustering

Using the pre-trained model as is for a new task

Removing the output layer of the pre-trained model and replacing it with a new output layer for a new task

Adding new layers to the pre-trained model and training the entire model on a new task

Using the pre-trained model for feature extraction and training a new model on top of the extracted features

Using the pre-trained model as is for a new task

Removing the output layer of the pre-trained model and replacing it with a new output layer for a new task

Adding new layers to the pre-trained model and training the entire model on a new task

Using the pre-trained model for feature extraction and training a new model on top of the extracted features

Computer vision and natural language processing

Time series analysis and regression

Clustering and classification

None of the above

A technique for transferring data between different databases

A technique for transferring data between different machine learning models

A technique for transferring data between different programming languages

A technique for transferring data between different cloud providers

Unsupervised and supervised

Online and offline

Active and passive

Manual and automatic

To reduce the amount of labeled data needed for a new task

To improve the accuracy of the model for a new task

To reduce the training time for a new task

All of the above