Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows the network to learn more complex features and representations.
B
Deeper networks always outperform shallower ones regardless of data quality.
C
It simplifies the model, making it easier to train.
D
Depth has no significant impact on performance; width is more important.
Understanding the Answer
Let's break down why this is correct
Answer
Increasing depth lets a network learn a hierarchy of features, starting with simple edges and textures in early layers and building up to complex shapes and object parts in later layers. Each additional layer can combine lower‑level patterns into richer, more abstract representations, which is especially useful for distinguishing subtle differences in images. Because deeper models have more capacity to capture these multi‑level abstractions, they can better separate classes that share low‑level similarities. For example, a shallow network might only detect edges, while a deeper one can first detect edges, then combine them into corners, then into faces, and finally into a full face, enabling accurate recognition. This layered abstraction is why deeper neural networks often outperform shallower ones on visual recognition tasks.
Detailed Explanation
A deeper network has more layers. Other options are incorrect because The idea that more depth always wins is a misconception; Adding layers does not simplify the model.
Key Concepts
Neural Network Depth
Feature Representation
Training Challenges
Topic
Network Depth Importance
Difficulty
medium 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.
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?
hardComputer-science
Practice
3
Question 3What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
easyComputer-science
Practice
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.
hardComputer-science
Practice
5
Question 5Why does increasing the depth of a neural network generally improve its performance in visual recognition tasks?
easyComputer-science
Practice
6
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.
mediumComputer-science
Practice
7
Question 7Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
hardComputer-science
Practice
8
Question 8Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
mediumComputer-science
Practice
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