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In plain baseline architectures, the degradation problem observed when increasing network depth is caused by vanishing gradients.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.4 Residual Network Experiments and Applications - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
ImageNet Classification and Model Variations - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Related
In the plain baseline architecture inspired by VGG, what rule is applied to the filter count when the feature map size is halved?
In plain baseline architectures, the degradation problem observed when increasing network depth is caused by vanishing gradients.
How does the plain baseline network perform downsampling, and what layer does it terminate with immediately before the 1000-way fully-connected layer?
Compare the performance of plain networks and residual networks when scaling depth from 18 to 34 layers on ImageNet. In your response, describe the degradation problem observed in the plain baselines, how residual networks resolve this issue, and what training behavior demonstrates that residual networks effectively benefit from added depth.