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It allows for the direct addition of layers without changing the output.
It reduces the learning rate needed for deep networks.
It helps maintain gradients during backpropagation in deeper networks.
It eliminates the need for activation functions.
Understanding the Answer
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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 in Deep Models
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Deep Dive: Identity Mapping in Deep Models
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Definition
Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.
Topic Definition
Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.
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