Machine Learning Quiz Questions

1. What is the main idea behind the concept of "task-agnostic" few-shot learning?

view answer: D. Training a model to adapt to new tasks without fine-tuning
Explanation: Task-agnostic few-shot learning aims to train a model that can adapt to new tasks without fine-tuning, enabling it to generalize well across a wide range of tasks.
2. How does active learning relate to few-shot learning?

view answer: A. Active learning is a technique used in few-shot learning
Explanation: Active learning is a technique that can be used in few-shot learning, where the model actively queries for informative examples to improve its performance with limited labeled data.
3. In few-shot learning, what is the purpose of a test set?

view answer: C. To evaluate the model's generalization performance
Explanation: The test set is used to evaluate the model's generalization performance on unseen data in few-shot learning.
4. What is the main idea behind the Omniglot dataset, often used in few-shot learning research?

view answer: A. A dataset of hand-written characters from multiple alphabets
Explanation: The Omniglot dataset consists of hand-written characters from multiple alphabets, designed to evaluate few-shot learning algorithms on a challenging classification task.
5. Which of the following is a technique used to prevent overfitting in few-shot learning?

view answer: D. All of the above
Explanation: Techniques like data augmentation, L1 regularization, and dropout can help prevent overfitting in few-shot learning by increasing the effective size of the dataset or reducing the complexity of the model.
6. How does curriculum learning relate to few-shot learning?

view answer: A. Curriculum learning is a technique used in few-shot learning
Explanation: Curriculum learning is a technique that can be used in few-shot learning, where the model is exposed to progressively more difficult tasks during training to improve generalization.
7. In few-shot learning, what is the purpose of a task distribution?

view answer: B. To define the range of tasks the model should learn
Explanation: The task distribution in few-shot learning defines the range of tasks the model should learn, which helps guide the training process and measure generalization ability.
8. What is the main idea behind Reptile, a meta-learning algorithm for few-shot learning?

view answer: D. Learning a good model initialization through gradient updates
Explanation: Reptile is a meta-learning algorithm that learns a good model initialization by averaging gradient updates across multiple tasks, which can then be fine-tuned for new tasks with few examples.
9. What is a common use case for few-shot learning?

view answer: A. Image classification
Explanation: Image classification is a common use case for few-shot learning, as it often involves recognizing new objects or categories with limited labeled examples.
10. Which of the following is an important factor to consider when choosing a distance metric for few-shot learning?

view answer: A. The type of data
Explanation: The type of data is an important factor when choosing a distance metric for few-shot learning, as different metrics may perform better on different types of data.
11. In few-shot learning, what is the purpose of a meta-dataset?

view answer: C. To simulate the process of learning new tasks
Explanation: A meta-dataset is used in few-shot learning to simulate the process of learning new tasks with limited examples, helping the model generalize better.
12. Which of the following is a common evaluation metric for few-shot learning models?

view answer: A. Accuracy
Explanation: Accuracy is a common evaluation metric for few-shot learning models, as it measures the proportion of correctly classified examples.
13. In few-shot learning, what is the purpose of an episodic training setup?

view answer: B. To simulate the process of learning new tasks
Explanation: An episodic training setup is used in few-shot learning to simulate the process of learning new tasks with limited examples, which helps the model to generalize better.
14. What is the main idea behind Relation Networks in few-shot learning?

view answer: D. Learning a relation score between examples and classes
Explanation: Relation Networks learn to compute a relation score between examples and classes, which is used to classify new examples based on their relation to the support set.
15. How does transfer learning relate to few-shot learning?

view answer: A. Transfer learning is a technique used in few-shot learning
Explanation: Transfer learning is a technique often used in few-shot learning, where a model is pre-trained on a large dataset and then fine-tuned on a smaller dataset to improve generalization.

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