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It allows for deeper networks without increasing complexity
It prevents overfitting by reducing the number of parameters
It facilitates the gradient flow by providing shortcuts
It eliminates the need for activation functions entirely
Understanding the Answer
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Identity mapping adds a shortcut that lets the gradient travel directly from later layers back to earlier ones. Other options are incorrect because The idea that identity mapping reduces complexity is a misconception; Identity mapping does not shrink the model.
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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