Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Implementing dropout regularization to prevent overfitting
B
Using a residual learning framework to allow gradients to flow more easily through the network
C
Increasing the batch size to improve training stability
D
Reducing the learning rate to avoid oscillations in loss
Understanding the Answer
Let's break down why this is correct
Answer
The degradation problem shows that adding more layers can make a deep network perform worse, not better. A common fix is to use residual connections, which let each block learn a small change to the input instead of a whole new mapping. By adding shortcuts that skip one or more layers, the network can still train deeper models without losing accuracy. For example, if a 10‑layer network drops accuracy, inserting a skip connection between the 3rd and 7th layers can keep the overall performance high. This residual design lets the deeper layers improve the model while avoiding the degradation that would otherwise happen.
Detailed Explanation
Residual learning adds shortcut connections that let the signal travel directly from earlier layers to later ones. Other options are incorrect because Dropout randomly turns off neurons during training; Increasing batch size smooths the gradient estimate.
Key Concepts
Degradation problem in deep networks
Residual learning framework
Overfitting
Topic
Degradation Problem in Deep Networks
Difficulty
medium 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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Practice
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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Practice
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 5In the context of deep learning, how does the degradation problem affect training efficiency and model complexity in neural networks?
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6
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 8Arrange the following steps in order to effectively address the degradation problem in deep networks: A) Implement residual connections, B) Increase network depth, C) Monitor training accuracy, D) Adjust learning rates accordingly.
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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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Practice
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