Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
width
B
depth
C
complexity
D
activation
Understanding the Answer
Let's break down why this is correct
Answer
Increasing the depth of a model generally improves its ability to integrate features and enhance classification accuracy in visual recognition tasks. Each added layer lets the network learn progressively more abstract representations, turning raw pixels into shapes, textures, and eventually object concepts. As depth grows, the model can capture finer details and more complex relationships, which helps it distinguish subtle differences between classes. For example, a 10‑layer CNN can separate cats from dogs more reliably than a 3‑layer network because it has more stages to refine the features. Therefore, deeper architectures usually yield better performance in image classification.
Detailed Explanation
Depth means adding more layers. Other options are incorrect because Width means adding more neurons in each layer; Complexity is a vague idea.
Key Concepts
Neural Network Architecture
Feature Integration
Classification Accuracy
Topic
Network Depth Importance
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?
easyComputer-science
Practice
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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3
Question 3Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
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Practice
4
Question 4What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
easyComputer-science
Practice
5
Question 5Arrange 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.
hardComputer-science
Practice
6
Question 6Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
easyComputer-science
Practice
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?
mediumComputer-science
Practice
8
Question 8Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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Practice
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