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Degradation Problem in Deep Networks
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How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?

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Learning Path

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Choose AnswerChoose the Best Answer

A

By allowing gradients to flow through layers without vanishing

B

By increasing the number of parameters excessively

C

By simplifying the architecture of the network

D

By reducing the input data dimensionality

Understanding the Answer

Let's break down why this is correct

Residual connections add a shortcut that lets the signal skip over layers. Other options are incorrect because Some think adding residuals means adding many new weights; A common misconception is that residuals simplify the whole network.

Key Concepts

residual connections
training efficiency
Topic

Degradation Problem in Deep Networks

Difficulty

medium level question

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understand

Deep Dive: Degradation Problem in Deep Networks

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Definition
Definition

The degradation problem in deep networks refers to the phenomenon where increasing network depth leads to saturation and rapid degradation in accuracy, despite not being caused by overfitting. This challenge highlights the complexities of optimizing deep models and the need for innovative approaches to prevent performance degradation.

Topic Definition

The degradation problem in deep networks refers to the phenomenon where increasing network depth leads to saturation and rapid degradation in accuracy, despite not being caused by overfitting. This challenge highlights the complexities of optimizing deep models and the need for innovative approaches to prevent performance degradation.

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