Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
By simplifying the network architecture
B
By reformulating layers to learn residual functions
C
By reducing the number of layers
D
By eliminating the need for optimization techniques
Understanding the Answer
Let's break down why this is correct
Answer
Residual learning lets each layer learn only the difference from its input, so the network can focus on correcting errors instead of reconstructing the whole signal. This shortcut connection adds the input directly to the output, making the gradient flow easily back through the network and preventing it from vanishing. Because each block can essentially be the identity function if needed, very deep stacks can still be optimized without getting stuck. For example, a 100‑layer network can be built from many 2‑layer residual blocks, each adding a small tweak, and the training process can still converge quickly. The result is that deeper models can be trained faster and with higher accuracy than plain deep networks.
Detailed Explanation
Residual learning lets each block learn a small difference, called a residual, between its input and output. Other options are incorrect because Some think residuals simplify the whole network; A common mistake is to think residuals reduce the number of layers.
Key Concepts
Residual Learning Framework
Deep Neural Networks
Optimization Techniques
Topic
Residual Learning Framework
Difficulty
easy 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 4Residual Learning Framework : Deeper Neural Networks :: Skip Connections : ?
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Question 5What is the primary reason that the residual learning framework improves the training of deeper neural networks?
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Question 6In 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 7A 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 8How does the residual learning framework improve the training of deep neural networks?
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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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