Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
improvement
B
degradation
C
saturation
D
overfitting
Understanding the Answer
Let's break down why this is correct
Answer
The degradation problem means that as we add more layers, the accuracy of the network starts to drop instead of staying the same or improving. This happens even though the model is not fitting the training data too closely, so it’s not overfitting. The issue is that deeper layers make it harder for gradients to flow back during training, which can make the later layers learn poorly. For example, a 30‑layer network may perform worse than a 10‑layer version on the same task because the extra layers make the optimization harder. The result is a decline in performance simply due to increased depth.
Detailed Explanation
When a network gets deeper, the signals that train it can get weaker and harder to pass through the layers. Other options are incorrect because Some people think adding more layers always makes a model better; Saturation usually refers to a neuron’s output becoming stuck at a maximum value.
Key Concepts
Degradation problem
Deep networks
Residual learning
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?
easyComputer-science
Practice
2
Question 2How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
mediumComputer-science
Practice
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?
hardComputer-science
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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 5Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
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Practice
6
Question 6Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
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Practice
7
Question 7Which of the following statements correctly describe strategies to mitigate the degradation problem in deep networks? Select all that apply.
hardComputer-science
Practice
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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Practice
9
Question 9When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
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10
Question 10What is the primary cause of the degradation problem in deep networks as they increase in depth?
easyComputer-science
Practice
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