Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
They can integrate more complex features across layers.
B
They are easier to train due to residual connections.
C
They require less data for training.
D
They reduce overfitting automatically.
Understanding the Answer
Let's break down why this is correct
Answer
Deeper neural networks can learn a hierarchy of features, starting with simple edges and textures in early layers and building up to complex shapes and whole objects in later layers. Each additional layer lets the network combine earlier features into richer, more abstract representations that are harder to capture with fewer layers. This hierarchy mirrors how humans recognize objects, making the network more robust to variations in lighting, pose, or background. For example, a shallow network might only detect that a cat has whiskers, while a deeper one can combine whisker, ear, and tail patterns to reliably recognize a cat even in a crowded scene. Thus, the main reason depth helps is that it enables the extraction of progressively more complex, discriminative features.
Detailed Explanation
Deeper networks stack many layers, each learning a new set of features. Other options are incorrect because Residual connections help gradients move through deep nets, making training easier; More layers mean more parameters, which usually need more examples to learn well.
Key Concepts
Neural Network Depth
Visual Recognition Performance
Residual Learning
Topic
Network Depth Importance
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary reason that the residual learning framework improves the training of deeper neural networks?
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2
Question 2Which 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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3
Question 3A 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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4
Question 4Why is increasing the depth of a neural network often beneficial for visual recognition tasks?
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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.
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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 8Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
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