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Degradation Problem in Deep Networks
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Arrange the following steps in order to effectively address the degradation problem in deep networks: A) Implement residual connections, B) Increase network depth, C) Monitor training accuracy, D) Adjust learning rates accordingly.

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A

B → A → C → D

B

A → B → C → D

C

B → C → A → D

D

A → C → B → D

Understanding the Answer

Let's break down why this is correct

First, you add more layers to explore deeper models. Other options are incorrect because Adding residual connections before making the network deeper means you are giving support to a network that still needs more layers; Monitoring accuracy before fixing the problem can make you think the network is fine when it is not.

Key Concepts

Degradation Problem in Deep Networks
Residual Learning Framework
Neural Network Training
Topic

Degradation Problem in Deep Networks

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easy level question

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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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