Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
The model is overfitting, so they should add more training data.
B
The degradation is due to the complexity of deeper networks; they should implement residual connections.
C
The model's performance is limited by the quality of the training data; they need to improve data labeling.
D
Deeper networks are always better; they should continue increasing depth without changes.
Understanding the Answer
Let's break down why this is correct
Answer
The model’s accuracy drops because deeper networks suffer from the degradation problem, where adding more layers makes the optimization harder and gradients vanish or explode, so the network can’t learn useful representations. When gradients shrink, the deeper layers learn almost nothing, so the deeper network actually performs worse than a shallower one. A common fix is to use residual (skip) connections, as in ResNet, which let the gradient flow directly to earlier layers and preserve the identity mapping. By adding these shortcut paths, the network can train deeper without losing accuracy, as shown when a 50‑layer ResNet outperforms a plain 50‑layer CNN on ImageNet. Thus, replacing plain layers with residual blocks or using batch normalization and proper initialization can mitigate the degradation.
Detailed Explanation
When a network gets very deep, the signals that travel backward during training can become very weak, a problem called vanishing gradients. Other options are incorrect because The idea that more depth means the model will overfit is a common mistake; Improving data labeling is useful, but it does not solve the problem of deep networks getting worse.
Key Concepts
Degradation Problem in Deep Networks
Residual Learning
Deep Neural Networks
Topic
Degradation Problem in Deep Networks
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1A team of researchers is developing a new convolutional neural network for classifying images of various objects. They notice that as they add more layers to the network, the accuracy begins to stagnate or even decrease. How can the team utilize the residual learning framework to improve their model's performance?
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2
Question 2A neural network architecture is being designed for an image recognition task. Considering the importance of network depth, which of the following approaches would most likely enhance the model's performance, particularly in feature integration and classification accuracy?
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Question 3Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
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Question 4In 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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5
Question 5A team of researchers is developing a deep neural network for image recognition, but they notice that the network struggles to learn effectively as they increase the number of layers. Which of the following strategies would best address the vanishing/exploding gradients problem they are facing?
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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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Question 7Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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