Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
True
B
False
Understanding the Answer
Let's break down why this is correct
Answer
False. Adding more layers can make a network able to learn more complicated patterns, but it also creates problems such as vanishing gradients, overfitting, and increased training time, so deeper does not guarantee better results. When a network gets too deep, the error signal can become too weak for the early layers to learn, causing the model to degrade in accuracy instead of improve. Regular techniques like skip connections or batch normalization help mitigate this, but the depth alone is not a silver bullet. For example, a 10‑layer network might outperform a 3‑layer one on a simple task, but a 100‑layer version without proper tricks may actually perform worse because the gradients vanish and the model overfits.
Detailed Explanation
Adding more layers can make a network harder to train. Other options are incorrect because The mistake is thinking depth alone guarantees better results.
Key Concepts
Degradation Problem in Deep Networks
Residual Learning Framework
Network Optimization Techniques
Topic
Degradation Problem in Deep Networks
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
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Question 1How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
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Question 2Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
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Question 3What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
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Question 4Arrange the following steps in the correct order to explain the importance of network depth in neural networks for visual recognition tasks: A) Network depth increases the capacity for feature extraction, B) Deeper networks can integrate complex features more effectively, C) The model achieves higher classification accuracy, D) Training becomes more challenging due to vanishing gradients.
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Question 5Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
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Question 6In the context of neural networks, increasing the _____ of a model generally improves its ability to integrate features and enhance classification accuracy in visual recognition tasks.
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Question 7Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
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Question 8Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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