Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows for the direct addition of layers without changing the output.
B
It reduces the learning rate needed for deep networks.
C
It helps maintain gradients during backpropagation in deeper networks.
D
It eliminates the need for activation functions.
Understanding the Answer
Let's break down why this is correct
Answer
Identity mapping lets a deep network keep the original input unchanged through a layer, which keeps the signal from vanishing or exploding as it passes through many weights. Because the input can travel unchanged, the gradient can flow backward without being squashed, so the network learns faster and more stably. This is why residual blocks, which add the input to the transformed output, work so well in very deep nets. For example, a 10‑layer network that adds its input at each layer can train a 200‑layer model without the training loss blowing up, while a plain network would struggle to learn anything at all. Thus, identity mapping mainly improves gradient flow and training stability.
Detailed Explanation
Identity mapping lets a layer copy its input to its output. Other options are incorrect because People think identity mapping only keeps the output the same, so they add layers without worry; Some believe identity mapping lowers the learning rate needed.
Key Concepts
Identity Mapping
Gradient Flow
Deep Neural Networks
Topic
Identity Mapping in Deep Models
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary purpose of identity mapping in deep learning models?
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Question 2How can identity mapping in deep neural networks enhance business applications such as customer segmentation and predictive analytics?
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3
Question 3In the context of deep learning, how can identity mapping enhance business applications while facilitating the scaling of models?
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4
Question 4A team of developers is working on a very deep neural network for image classification. They notice that as they add more layers, the training accuracy starts to decrease. To address this issue, they decide to implement identity mapping. How does this technique help improve the training process in their model?
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5
Question 5How does identity mapping in deep neural networks help with optimization during training?
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6
Question 6Identity Mapping in deep learning models is to optimizing training as a navigation system is to _____?
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7
Question 7You are designing a deep neural network to improve image recognition accuracy. Which of the following strategies would best utilize identity mapping to address common issues of training deep networks?
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8
Question 8In the context of deep learning, identity mapping is primarily used to maintain the ________ from shallower models, aiding in the training of deeper networks.
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9
Question 9Why does identity mapping in deep neural networks help improve training performance in very deep models?
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