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Identity mapping helps mitigate the vanishing gradient problem in very deep networks.
Identity mapping requires the addition of extra non-linear activations to improve performance.
Identity mapping allows the training of deeper neural networks without significantly degrading performance.
Identity mapping can be applied only in convolutional neural networks.
Identity mapping facilitates the optimization process by maintaining the original input through residual connections.
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Identity mapping keeps the original input unchanged through a shortcut path. Other options are incorrect because Identity mapping does not by itself solve the vanishing gradient problem; Identity mapping does not add extra nonlinear activations.
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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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