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Question & Answer
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Introducing skip connections can help gradients flow better during training.
Simply increasing the number of neurons in a layer always improves performance.
Using batch normalization can reduce internal covariate shift and improve training dynamics.
Residual networks (ResNets) allow deeper architectures without suffering from degradation.
Training with larger datasets eliminates the need for architectural innovations.
Understanding the Answer
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Skip connections let the gradient skip layers, so the signal can travel farther and the network learns easier. Other options are incorrect because Adding more neurons can make the model too complex and cause it to memorize training data instead of learning useful patterns; Even with a huge amount of data, the network still needs a good design to learn efficiently.
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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