Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It leads to decreased model performance, and empirical validation helps by providing real-world data to test against.
B
It causes overfitting, and empirical validation reduces it by increasing model complexity.
C
It results in underfitting, and empirical validation enhances the model size.
D
It increases training time, and empirical validation improves it by simplifying the architecture.
Understanding the Answer
Let's break down why this is correct
Answer
The main problem with degradation in deep networks is that adding more layers can make training harder, so the accuracy actually drops instead of staying the same or improving. This happens because the deeper layers can learn useless or noisy features that hurt overall performance. Empirical validation, such as testing on a separate validation set while training, lets us see when adding layers stops helping and starts hurting. By monitoring validation loss and accuracy, we can stop adding layers or adjust regularization when performance plateaus or worsens. For example, if a 20‑layer network shows lower validation accuracy than a 10‑layer one, we can conclude that the deeper network is degrading and revise the architecture.
Detailed Explanation
Adding more layers can make a network perform worse on training data. Other options are incorrect because The problem is not about overfitting; Degradation is not underfitting.
Key Concepts
empirical validation
Topic
Degradation Problem in Deep Networks
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
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Question 2How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
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3
Question 3Which 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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4
Question 4In the context of deep learning, how does the degradation problem affect training efficiency and model complexity in neural networks?
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5
Question 5Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
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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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