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DeepCaps: Going Deeper With Capsule Networks

CVPR 2019 · Architectures and efficiency › CNN design · approach: Filter design and trained-filter analysis · 245 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the deeper DeepCaps architecture instead of Sabour et al. CapsNetCapsule Networks · CIFAR10accuracy: 89.40% -> 91.01%Helped
Use the deeper DeepCaps architecture instead of Sabour et al. CapsNetCapsule Networks · SVHNaccuracy: 95.70% -> 97.16%Helped
Use the deeper DeepCaps architecture instead of HitNetCapsule Networks · Fashion-MNISTaccuracy: 92.30% -> 94.46%Helped
Use DeepCaps instead of the best reported state-of-the-art baselineImage classification · MNISTaccuracy: 99.79% -> 99.72%Hurt
Use DeepCaps instead of Sabour et al. CapsNetCapsule Networks · MNISTaccuracy: 99.75% -> 99.72%Hurt
Use a weighted ensemble of 7 models rather than a single DeepCaps modelDeepCaps · CIFAR10accuracy: 91.01% -> 92.74%Helped
Use a weighted ensemble of 7 models rather than a single DeepCaps modelDeepCaps · SVHNaccuracy: 97.16% -> 97.56%Helped
Use a weighted ensemble of 7 models rather than a single DeepCaps modelDeepCaps · Fashion-MNISTaccuracy: 94.46% -> 94.73%Helped
Use DeepCaps instead of CapsNetCapsule Networks on CIFAR10 · CIFAR10number of parameters: 22.48 Million -> 7.22 MillionHelped
Use DeepCaps instead of CapsNetCapsule Networks · inference on NVIDIA V100 GPUinference time: 2.86 ms -> 1.38 msHelped

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