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Identity Mapping in Deep Models
easy

In 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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Learning Path

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Choose AnswerChoose the Best Answer

A

identity mapping

B

feature extraction

C

gradient descent

D

activation function

Understanding the Answer

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Identity mapping keeps a layer’s output the same as its input. Other options are incorrect because Feature extraction is about turning raw data into useful patterns, not copying; Gradient descent is a method to adjust weights to reduce error.

Key Concepts

Identity Mapping
Neural Network Training
Residual Learning
Topic

Identity Mapping in Deep Models

Difficulty

easy level question

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Definition
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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