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Question & Answer
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Deeper networks always improve performance metrics due to increased capacity.
Deeper networks can lead to worse performance metrics because of the degradation problem, where training accuracy decreases despite the increase in depth.
Increasing depth has no effect on the performance metrics if the network is properly regularized.
Performance metrics are only affected by the width of the network, not the depth.
Understanding the Answer
Let's break down why this is correct
Adding more layers can make the model harder to train. Other options are incorrect because The idea that more depth always improves performance is a misconception; Regularization can help, but it does not eliminate the degradation problem.
Key Concepts
Degradation Problem in Deep Networks
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Deep Dive: Degradation Problem in Deep Networks
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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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