Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Use a shallow network with fewer layers to prevent overfitting.
B
Implement a deeper network with residual connections to facilitate training.
C
Increase the learning rate significantly to train the network faster.
D
Use dropout layers extensively throughout a shallow network to improve generalization.
Understanding the Answer
Let's break down why this is correct
Answer
Adding more layers can help a network learn more complex patterns, but it must be done carefully so the gradients still flow. A common way to deepen a model while keeping training stable is to use residual or shortcut connections that let the signal jump over blocks of layers; this lets the network learn both low‑level and high‑level features without vanishing gradients. Pairing these blocks with batch normalization and a moderate learning rate keeps the training process smooth. For example, a 50‑layer ResNet can extract fine textures in early layers and combine them into abstract shapes in later layers, improving both feature integration and classification accuracy. Thus, a deeper architecture with skip connections is the most likely approach to boost performance.
Detailed Explanation
Adding more layers lets the network learn more complex patterns. Other options are incorrect because Many think a shallow network always avoids overfitting, but it also misses complex patterns; A higher learning rate is often mistaken for faster training, but it can make learning unstable.
Key Concepts
Network Depth
Residual Learning
Overfitting
Topic
Network Depth Importance
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following statements correctly describe the importance of network depth in neural networks for visual recognition tasks? Select all that apply.
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Question 2Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
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3
Question 3What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
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4
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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5
Question 5A tech startup is developing a new image recognition app to identify objects in real-time from live video feeds. The team is deciding whether to use a shallow neural network with fewer layers or a deeper neural network with more layers. Based on your understanding of network depth importance, what should they consider when choosing the network architecture?
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6
Question 6Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
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7
Question 7In 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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8
Question 8A 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 9You are designing a deep neural network to improve image recognition accuracy. Which of the following strategies would best utilize identity mapping to address common issues of training deep networks?
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