Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Increased difficulty in optimizing the network
B
Overfitting due to excessive parameters
C
Lack of sufficient training data
D
Simple increase in model capacity without improvement
Understanding the Answer
Let's break down why this is correct
Answer
The main reason deeper nets get worse is that the gradients that drive learning become very small or explode as they travel back through many layers, a problem called vanishing or exploding gradients. This makes the network hard to optimize; the added layers do not help because the signal that tells earlier layers how to adjust is lost. As a result, the training error actually rises when you add more layers, even though the model has more capacity. For example, a 50‑layer network may have a higher training loss than a 20‑layer version because the gradients from the last layer cannot reach the first layers effectively. This difficulty in propagating error signals is the primary cause of the degradation problem.
Detailed Explanation
When a network gets deeper, the path that learning signals travel becomes longer. Other options are incorrect because Many think more parameters mean the model will overfit, but degradation happens even when data is plenty; Some believe that not having enough data makes deep networks fail, but depth alone can cause problems.
Key Concepts
Degradation Problem
Deep Learning Optimization
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
Practice
4
Question 4What is a primary cause of the vanishing gradients problem in deep neural networks?
mediumComputer-science
Practice
5
Question 5Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
hardComputer-science
Practice
6
Question 6Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
easyComputer-science
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.
easyComputer-science
Practice
9
Question 9When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
mediumComputer-science
Practice
10
Question 10The degradation problem in deep networks primarily refers to the issue where increasing network depth leads to performance ____, rather than overfitting.
easyComputer-science
Practice
Ready to Master More Topics?
Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.