Semi-Supervised Learning QUIZ - MCQ QUESTIONS AND ANSWERS

Question: 1

What is the primary goal of semi-supervised learning?

Question: 2

In the context of semi-supervised learning, what is the "smoothness assumption"?

Question: 3

Which of the following methods is commonly used in semi-supervised learning to propagate labels from labeled to unlabeled data points?

Question: 4

How does the self-training approach in semi-supervised learning work?

Question: 5

What is the main difference between transductive and inductive semi-supervised learning?

Question: 6

Which of the following is NOT a typical challenge faced in semi-supervised learning?

Question: 7

In the context of semi-supervised learning, what is "co-training"?

Question: 8

Which of the following is a primary advantage of using generative models in semi-supervised learning?

Question: 9

In the context of semi-supervised learning, what is "pseudo-labeling"?

Question: 10

Which of the following is NOT a common approach used in semi-supervised learning?

Question: 11

How does the "cluster assumption" relate to semi-supervised learning?

Question: 12

What is the main advantage of using semi-supervised learning over supervised learning?

Question: 13

Which of the following is a common strategy to combine labeled and unlabeled data in semi-supervised learning?

Question: 14

What is the main challenge in using self-training for semi-supervised learning?

Question: 15

In the context of semi-supervised learning, what is the purpose of "consistency regularization"?

Question: 16

In semi-supervised learning, which of the following is an advantage of using graph-based methods?

Question: 17

Which of the following is a disadvantage of using semi-supervised learning?

Question: 18

What is the main assumption underlying most semi-supervised learning algorithms?

Question: 19

Which of the following is NOT a common assumption in semi-supervised learning?

Question: 20

In the context of semi-supervised learning, what is the "manifold assumption"?

Question: 21

Which of the following techniques is typically NOT used for semi-supervised learning?

Question: 22

In which scenario is semi-supervised learning most useful?

Question: 23

What is the main advantage of using transductive semi-supervised learning over inductive semi-supervised learning?

Question: 24

How do generative models differ from discriminative models in the context of semi-supervised learning?

Question: 25

Which of the following is a common approach to incorporating unlabeled data into semi-supervised learning algorithms based on deep learning?

Question: 26

In the context of semi-supervised learning, which of the following is an advantage of using discriminative models over generative models?

Question: 27

In the context of semi-supervised learning, what is the "low-density separation" assumption?

Question: 28

What is a potential drawback of using graph-based methods in semi-supervised learning?

Question: 29

Which of the following techniques can be used to address the risk of propagating incorrect labels in semi-supervised learning?

Question: 30

In semi-supervised learning, why is it important to have a diverse set of labeled instances?