Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
By allowing layers to learn the residual mapping instead of the original mapping
B
By reducing the number of layers in the network to simplify training
C
By eliminating the use of activation functions in the network
D
By increasing the learning rate to speed up convergence
Understanding the Answer
Let's break down why this is correct
Answer
Residual learning lets a deep network learn a small adjustment instead of an entire mapping. By adding a shortcut that passes the input directly to later layers, the network only has to learn the difference between the desired output and the input, which is often easier. This shortcut keeps the gradient flowing straight through, preventing it from vanishing as it moves backward through many layers. As a result, very deep models can be trained without the usual degradation in accuracy. For example, a 50‑layer network that would normally drop in performance can now learn a residual function and achieve higher accuracy than a plain 50‑layer network.
Detailed Explanation
The network learns a small difference, called the residual, that is added to a shortcut connection. Other options are incorrect because Some think it cuts layers to make training easier; Activation functions are still used to add non‑linearity.
Key Concepts
Residual Learning Framework
Deep Neural Networks
Optimization Techniques
Topic
Residual Learning Framework
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
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Question 1How does the Residual Learning Framework enhance Continuous Improvement in organizations through Data-Driven Decision Making?
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Question 2How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
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Question 3Which 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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Question 4How does the residual learning framework enhance the training of deeper neural networks?
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Question 5Residual Learning Framework : Deeper Neural Networks :: Skip Connections : ?
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Question 6What is the primary reason that the residual learning framework improves the training of deeper neural networks?
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Question 7In 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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Question 8A 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?
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Question 9Order the steps in the Residual Learning Framework that enable effective training of deeper neural networks.
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