Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Improved model training
B
Increased overfitting
C
Reduced model depth
D
Simpler network architecture
Understanding the Answer
Let's break down why this is correct
Answer
Residual learning is the technique that tackles the degradation problem in deep networks. By adding shortcut connections that let each block learn only the difference from its input, the network can train much deeper models without accuracy dropping. The shortcut lets gradients flow directly, so the layers can focus on refining the signal rather than relearning it. For example, a 50‑layer plain CNN might see its test accuracy drop to 70 %, while the same depth with residual connections can reach 85 % because each block learns a small residual instead of the whole mapping. Thus, residual learning mitigates performance degradation and improves overall accuracy.
Detailed Explanation
Residual learning adds shortcut links that let the signal pass directly to later layers. Other options are incorrect because Some think residual learning makes the model overfit because it adds more parameters; A common mistake is to believe residual learning reduces the number of layers.
Key Concepts
Degradation Problem
Residual Learning
Deep Neural Networks
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 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 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 7Arrange 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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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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