Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows layers to learn residual functions instead of direct mappings
B
It simplifies the network by reducing the number of layers
C
It uses fewer parameters, making the model less complex
D
It eliminates the need for backpropagation
Understanding the Answer
Let's break down why this is correct
Answer
Residual learning lets a deep network learn the difference between its input and the desired output rather than the output itself, so the network can focus on correcting small errors. This makes the gradients flow more easily backward through many layers because the shortcut connections provide a direct path for the signal, reducing vanishing or exploding gradients. When a layer learns only a residual, it can quickly approximate the identity function if needed, which prevents degradation of performance as layers are added. For example, a 50‑layer network that adds a residual term can still propagate the original input unchanged, allowing deeper layers to refine the result without losing the initial information. Consequently, deeper networks train faster and achieve higher accuracy.
Detailed Explanation
Residual learning lets each block learn a small change, called a residual, instead of trying to build the whole mapping from scratch. Other options are incorrect because Many think residual networks are smaller, but they actually add more layers; Residual networks do not cut parameters; they often add more because of the extra shortcut connections.
Key Concepts
Residual Learning Framework
Deep Neural Networks
Optimization Techniques
Topic
Residual Learning Framework
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
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Question 2Which 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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3
Question 3How does the residual learning framework enhance the training of deeper neural networks?
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4
Question 4Residual Learning Framework : Deeper Neural Networks :: Skip Connections : ?
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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 6A 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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7
Question 7How does the residual learning framework improve the training of deep neural networks?
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8
Question 8Order the steps in the Residual Learning Framework that enable effective training of deeper neural networks.
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9
Question 9What is the primary reason deeper neural networks tend to improve performance in visual recognition tasks?
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