Few-Shot Learning QUIZ QUESTIONS

Training a model without any prior knowledge of the task

Training a model to perform well on a wide range of tasks

Training a model without considering the specific task requirements

Training a model to adapt to new tasks without fine-tuning

Active learning is a technique used in few-shot learning

Few-shot learning is a technique used in active learning

Active learning and few-shot learning are the same concept

Active learning and few-shot learning are unrelated concepts

To train the model on a new task

To provide additional information for the model

To evaluate the model's generalization performance

To store and retrieve information during training

A dataset of hand-written characters from multiple alphabets

A dataset of natural images with multiple object categories

A dataset of time series data from various domains

A dataset of text data in multiple languages

Data augmentation

L1 regularization

Dropout

All of the above

Curriculum learning is a technique used in few-shot learning

Few-shot learning is a technique used in curriculum learning

Curriculum learning and few-shot learning are the same concept

Curriculum learning and few-shot learning are unrelated concepts

To balance the classes in the dataset

To define the range of tasks the model should learn

To determine the order in which tasks should be learned

To measure the performance of the model on different tasks

Learning a shared representation for all classes

Learning class prototypes to represent each category

Learning to compare the similarity between pairs of examples

Learning a good model initialization through gradient updates

Image classification

Time series forecasting

Text summarization

Dimensionality reduction

The type of data

The size of the dataset

The complexity of the model

The number of classes

To train the model on a new task

To provide a large set of examples for training

To simulate the process of learning new tasks

To store and retrieve information during training

Accuracy

Precision

Recall

F1 score

To improve model interpretability

To simulate the process of learning new tasks

To increase the model capacity

To reduce the training time

Learning a separate model for each class

Learning a shared representation for all classes

Learning to compare the similarity between pairs of examples

Learning a relation score between examples and classes

Transfer learning is a technique used in few-shot learning

Few-shot learning is a technique used in transfer learning

Transfer learning and few-shot learning are the same concept

Transfer learning and few-shot learning are unrelated concepts

A memory-augmented neural network

A prototypical network

An attention mechanism

A Siamese network

Learning a separate model for each class

Learning a shared representation for all classes

Learning to compare the similarity between pairs of examples

Learning a hierarchical structure of classes

Learning a separate model for each class

Learning a shared representation for all classes

Learning class prototypes to represent each category

Learning a hierarchical structure of classes

To train the model on a new task

To provide additional information for the model

To evaluate the model's performance on the new task

To store and retrieve information during training

To provide a large set of examples for training

To provide a small set of examples for model adaptation

To evaluate the model's generalization performance

To store and retrieve information during training

Euclidean distance

Manhattan distance

Cosine similarity

All of the above

Classifying K examples into N categories

Classifying N examples into K categories

Classifying K examples with N features

Classifying N examples with K features

To store and retrieve information for improved generalization

To increase the model capacity

To reduce the training time

To improve model interpretability

Learning from no labeled examples

Learning from a single labeled example

Learning from a very large dataset

Learning without any prior knowledge

Reduces the need for large labeled datasets

Increases model complexity

Improves model interpretability

Reduces the risk of overfitting

Dropout

Data augmentation

Meta-learning

Batch normalization

To optimize the learning rate

To learn a good representation for the task

To learn how to learn

To avoid overfitting

MAML (Model-Agnostic Meta-Learning)

LASSO (Least Absolute Shrinkage and Selection Operator)

AdaBoost

Random Forest

Learning from a large number of labeled examples

Learning from a small number of labeled examples

Learning from unlabeled data

Learning from human demonstrations

Overfitting

Underfitting

High computation cost

Scalability