Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It causes the model to overfit the training data too quickly.
B
Deeper networks can suffer from vanishing gradients, making training ineffective.
C
More layers always improve the model's capacity to learn.
D
Increased depth requires more data, which is not always available.
Understanding the Answer
Let's break down why this is correct
Answer
Adding more layers gives the network more capacity, but it also makes training harder because the signal that tells each layer how to change must travel through many weights. As depth grows, gradients can shrink or explode, so the early layers learn very slowly or not at all, a problem called vanishing or exploding gradients. Moreover, deeper models are more flexible and can fit the training data too well, leading to overfitting and poor generalization. For example, a 3‑layer network might achieve 90 % accuracy, while a 20‑layer version can get stuck at 70 % because its gradients vanish and it overfits. Techniques like batch normalization or residual connections are often needed to keep deep networks trainable.
Detailed Explanation
When a network has many layers, the error signal that tells the model how to change its weights travels through each layer during training. Other options are incorrect because People think more layers mean the model will fit the training data too fast, but that happens when the data is too small, not just because the network is deeper; It is tempting to think that more layers always give a better model, but deeper networks are harder to train.
Key Concepts
Degradation problem in deep networks
Vanishing gradients
Overfitting
Topic
Degradation Problem in Deep Networks
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
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 4Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
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Question 5In 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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6
Question 6Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
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7
Question 7A data scientist is tasked with building a deep neural network to classify images of animals. They notice that as they increase the depth of the network, the accuracy of their model begins to degrade significantly. What is the most likely reason for this degradation, and what approach could they take to mitigate it?
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Question 8What is the primary cause of the degradation problem in deep networks as they increase in depth?
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