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Question & Answer
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improvement
degradation
saturation
overfitting
Understanding the Answer
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When a network gets deeper, the signals that train it can get weaker and harder to pass through the layers. Other options are incorrect because Some people think adding more layers always makes a model better; Saturation usually refers to a neuron’s output becoming stuck at a maximum value.
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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