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The degradation problem in deep plain networks is primarily caused by vanishing or exploding gradients that prevent convergence from the start of training.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.3 Deep Residual Network Architecture - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
The Degradation Problem in Deep Networks - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Related
How does the network degradation problem fundamentally differ from overfitting as depth increases in plain convolutional neural networks?
The degradation problem in deep plain networks is primarily caused by vanishing or exploding gradients that prevent convergence from the start of training.
When comparing an 18-layer plain network to a 34-layer plain network trained on ImageNet, what error pattern demonstrates the network degradation phenomenon?
Explain the network degradation phenomenon observed in deep plain neural networks. In your response, contrast degradation with both overfitting and vanishing/exploding gradients, and reference empirical findings from benchmark datasets.