Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Deeper networks always improve performance metrics due to increased capacity.
B
Deeper networks can lead to worse performance metrics because of the degradation problem, where training accuracy decreases despite the increase in depth.
C
Increasing depth has no effect on the performance metrics if the network is properly regularized.
D
Performance metrics are only affected by the width of the network, not the depth.
Understanding the Answer
Let's break down why this is correct
Answer
Adding more layers to a neural network can give the model more capacity to learn complex patterns, but it also makes the training problem harder. As the depth grows, gradients that are back‑propagated become smaller, so the earlier layers learn very slowly or not at all, a phenomenon called the degradation problem. When this happens the training error can actually increase, and the overall accuracy of the network can drop instead of improving. For example, a 10‑layer network might reach 90 % accuracy, but a 20‑layer version without special tricks can fall to 85 % because the deeper layers are not learning effectively. Thus, simply making a network deeper can hurt performance unless techniques like residual connections or better optimization are used.
Detailed Explanation
Adding more layers can make the model harder to train. Other options are incorrect because The idea that more depth always improves performance is a misconception; Regularization can help, but it does not eliminate the degradation problem.
Key Concepts
performance metrics
network depth
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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Question 2Which 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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3
Question 3In the context of deep learning, how does the degradation problem affect training efficiency and model complexity in neural networks?
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Question 4Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
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5
Question 5Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
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Question 6Which of the following statements correctly describe strategies to mitigate the degradation problem in deep networks? Select all that apply.
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7
Question 7When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
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8
Question 8The degradation problem in deep networks primarily refers to the issue where increasing network depth leads to performance ____, rather than overfitting.
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Question 9Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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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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