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It increases the number of parameters without affecting the model's ability to learn.
It allows gradients to flow more easily through the network during backpropagation.
It reduces the complexity of the model by removing layers.
It ensures that each layer learns unique features independently.
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Identity mapping lets the input signal pass straight through layers. Other options are incorrect because Adding more parameters does not guarantee better learning; Identity mapping does not remove layers; it adds extra connections that skip layers.
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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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