Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
B → A → C → D
B
A → B → C → D
C
B → C → A → D
D
A → C → B → D
Understanding the Answer
Let's break down why this is correct
Answer
To tackle degradation, first monitor training accuracy (C) so you can see if performance drops as the network deepens. Next, adjust the learning rates (D) to stabilize training. If the problem persists, increase the network depth (B) to gain expressive power. Finally, add residual connections (A) to allow gradients to flow more easily and prevent further degradation. This sequence ensures you diagnose, stabilize, expand, and then structurally fix the network.
Detailed Explanation
First, you add more layers to explore deeper models. Other options are incorrect because Adding residual connections before making the network deeper means you are giving support to a network that still needs more layers; Monitoring accuracy before fixing the problem can make you think the network is fine when it is not.
Key Concepts
Degradation Problem in Deep Networks
Residual Learning Framework
Neural Network Training
Topic
Degradation Problem in Deep Networks
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary issue associated with the degradation problem in deep networks, and how can empirical validation help mitigate this issue?
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2
Question 2How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
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3
Question 3How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
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4
Question 4Which of the following strategies most effectively addresses the degradation problem in deep networks while considering training efficiency, optimization techniques, and scalability issues?
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5
Question 5Arrange the following steps in addressing the vanishing/exploding gradients problem in deep neural networks from first to last: A) Implement normalization techniques, B) Train the network, C) Initialize weights appropriately, D) Monitor gradient behavior during training.
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Question 6Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
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7
Question 7Which of the following statements correctly describe strategies to mitigate the degradation problem in deep networks? Select all that apply.
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8
Question 8When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
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9
Question 9The degradation problem in deep networks primarily refers to the issue where increasing network depth leads to performance ____, rather than overfitting.
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10
Question 10What is the primary cause of the degradation problem in deep networks as they increase in depth?
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