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Degradation Problem in Deep Networks
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A data scientist is tasked with building a deep neural network to classify images of animals. They notice that as they increase the depth of the network, the accuracy of their model begins to degrade significantly. What is the most likely reason for this degradation, and what approach could they take to mitigate it?

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A

The model is overfitting, so they should add more training data.

B

The degradation is due to the complexity of deeper networks; they should implement residual connections.

C

The model's performance is limited by the quality of the training data; they need to improve data labeling.

D

Deeper networks are always better; they should continue increasing depth without changes.

Understanding the Answer

Let's break down why this is correct

When a network gets very deep, the signals that travel backward during training can become very weak, a problem called vanishing gradients. Other options are incorrect because The idea that more depth means the model will overfit is a common mistake; Improving data labeling is useful, but it does not solve the problem of deep networks getting worse.

Key Concepts

Degradation Problem in Deep Networks
Residual Learning
Deep Neural Networks
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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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