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Scaling network depth across 50-layer, 101-layer, and 152-layer ResNets results in the degradation problem and lower accuracy.
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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
Deeper Bottleneck Architectures - 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
Match each network architecture to its corresponding computational complexity (FLOPs) or structural design specification.
How should the team address these two concerns using the known computational complexity and scaling properties of bottleneck ResNets?
How is the ResNet-50 architecture structurally derived from the 34-layer ResNet baseline?
Scaling network depth across 50-layer, 101-layer, and 152-layer ResNets results in the degradation problem and lower accuracy.
Across which specific feature map sizes are significantly more 3-layer blocks stacked to build ResNet-101 and ResNet-152?
Compare the computational complexity of ResNet-152 with VGG-16 and VGG-19, and describe the impact of scaling to this depth on model degradation and accuracy.