Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Shallow Neural Networks
B
Residual Functions
C
Network Depth
D
Feature Extraction
Understanding the Answer
Let's break down why this is correct
Answer
In a residual learning framework, the idea is that a deep network learns only the difference between the desired mapping and the identity function, so the overall mapping is the sum of that difference and the original input. Skip connections realize this idea by literally “skipping” over one or more layers and feeding the input straight to a later layer, which lets the network add a small correction to the input rather than learning a whole new function. Because the input can bypass the nonlinear layers, gradients can flow directly through the network, reducing the vanishing‑gradient problem and making very deep models trainable. For example, a 50‑layer residual block can add a small residual to its 10‑layer input, allowing the block to act like an identity mapping if the residual is zero. Thus, skip connections are the mechanism that implements residual learning in deep neural networks.
Detailed Explanation
Skip connections let the network learn a residual function, which is the difference between the desired output and the input. Other options are incorrect because The idea that skip connections are only useful for shallow networks is wrong; Thinking that skip connections simply increase network depth is a misconception.
Key Concepts
Residual Learning Framework
Skip Connections
Deeper Neural Networks
Topic
Residual Learning Framework
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following statements accurately describe the benefits of using the Residual Learning Framework in deep neural networks? Select all that apply.
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Practice
2
Question 2In the context of the residual learning framework, the primary purpose of introducing skip connections is to enable the network to learn the __________ of the desired output with respect to its inputs.
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3
Question 3How does the residual learning framework enhance the training of deeper neural networks?
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4
Question 4What is the primary reason that the residual learning framework improves the training of deeper neural networks?
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5
Question 5In the context of deep learning, which of the following scenarios best exemplifies the application of the residual learning framework to improve neural network training efficiency?
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6
Question 6How does the residual learning framework improve the training of deep neural networks?
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7
Question 7Order the steps in the Residual Learning Framework that enable effective training of deeper neural networks.
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Practice
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