Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows for more complex feature extraction.
B
Deeper networks are easier to train than shallow ones.
C
It reduces the number of parameters in the model.
D
It eliminates the need for data augmentation.
Understanding the Answer
Let's break down why this is correct
Answer
Adding more layers lets a network build more complex, hierarchical features, starting with simple edges and progressing to shapes, objects, and whole scenes. Each layer can combine lower‑level patterns into higher‑level ones, so deeper networks capture richer visual representations. With more depth, the model can learn subtle distinctions that shallow models miss, such as texture variations or occluded parts. For example, a 2‑layer network might only detect bright corners, while a 10‑layer network can first find edges, then textures, then recognize a cat’s ear shape. Thus, deeper networks generally achieve higher accuracy on visual tasks because they can represent and learn more detailed, abstract features.
Detailed Explanation
More layers let the network learn higher‑level patterns. Other options are incorrect because People think deeper nets are easier to train, but they often suffer from vanishing gradients; A deeper network usually has more parameters, not fewer.
Key Concepts
Neural Network Depth
Feature Extraction
Visual Recognition
Topic
Network Depth Importance
Difficulty
easy 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 3Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
mediumComputer-science
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 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
Ready to Master More Topics?
Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.