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CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces

NeurIPS 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Loss design and classifier theory · 56 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add orthogonal capsule projection (c=8)ResNet-110 · CIFAR10error rate: 6.41 -> 5.19Helped
Add orthogonal capsule projection (c=8)ResNet-110 · CIFAR100error rate: 27.22 -> 21.93Helped
Add orthogonal capsule projection (c=2 or c=8)ResNet-110 · SVHNerror rate: 2.01 -> 1.79Helped
Increase capsule dimension from c=4 to c=8 (ablation)CapProNet+ResNet-110 · CIFAR100error rate: 22.45 -> 21.93Helped
Increase capsule dimension from c=4 to c=8 (ablation)CapProNet+ResNet-110 · SVHNerror rate: 1.82 -> 1.79No clear effect
Increase capsule dimension from c=4 to c=8 (ablation)CapProNet+WideResNet-28 · CIFAR100error rate: 19.98 -> 19.83Helped
Add capsule projection (c=4)WideResNet-28 · CIFAR10error rate: 4.17 -> 3.64Helped
Add capsule projection (c=8)Densenet-BC k=12 · CIFAR100error rate: 22.27 -> 21.19Helped
Add capsule projection (c=8)Resnet50 · ImageNet validation, single crop 224x224Top-1 error: 24.09 -> 23.203Helped
Add capsule projection (c=4)Resnet101 · ImageNet validation, single crop 224x224Top-1 error: 22.81 -> 21.89Helped
Replace orthogonal capsule projection with grouped output neurons (ablation)ResNet-110 · CIFAR10error rate: 5.24 -> 6.26Hurt
Increase capsule dimension from c=4 to c=8 (ablation)CapProNet+WideResNet-28 · CIFAR10error rate: 3.64 -> 3.85Hurt

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