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Degradation Problem in Deep Networks
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Which of the following strategies most effectively addresses the degradation problem in deep networks while considering training efficiency, optimization techniques, and scalability issues?

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A

Implementing batch normalization and dropout

B

Using a single-layer perceptron for all tasks

C

Reducing the number of training epochs drastically

D

Applying gradient descent without any modifications

Understanding the Answer

Let's break down why this is correct

Batch normalization normalizes each layer’s output, keeping values in a stable range and reducing internal covariate shift. Other options are incorrect because The idea that a single-layer perceptron can solve all tasks is a misconception; Reducing training epochs drastically assumes that time is the only problem.

Key Concepts

training efficiency
optimization techniques
scalability issues.
Topic

Degradation Problem in Deep Networks

Difficulty

hard level question

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understand

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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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