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Question & Answer
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Increased difficulty in optimizing the network
Overfitting due to excessive parameters
Lack of sufficient training data
Simple increase in model capacity without improvement
Understanding the Answer
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When a network gets deeper, the path that learning signals travel becomes longer. Other options are incorrect because Many think more parameters mean the model will overfit, but degradation happens even when data is plenty; Some believe that not having enough data makes deep networks fail, but depth alone can cause problems.
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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