Research map / Architectures and efficiency / CNN design
Group Equivariant Capsule Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| combine the capsule network with pose-guided convolutions (ablation) | Group equivariant capsule network · MNIST rot. (50k) | accuracy: 92.30% -> 98.42% | Helped |
| combine the capsule network with pose-guided convolutions (ablation) | Group equivariant capsule network · AffNist | accuracy: 81.64% -> 89.10% | Helped |
| combine the capsule network with pose-guided convolutions (ablation) | Group equivariant capsule network · MNIST rot. (10k) | accuracy: 90.19% -> 97.40% | Helped |
| add reconstruction loss (ablation) | Capsule pose extraction · MNIST pose extraction | average pose error [degree]: 28.32 -> 16.21 | Helped |
| use capsule pose routing instead of naive hierarchical pose averaging (ablation) | MNIST pose extraction · MNIST pose extraction | average pose error [degree]: 70.92 -> 28.32 | Helped |
Closest papers by meaning
- Building Deep Equivariant Capsule Networks (ICLR 2020 · similarity 0.95)
- Enabling Equivariance for Arbitrary Lie Groups (CVPR 2022 · similarity 0.89)
- Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts (ICML 2024 · similarity 0.89)
- Probing Equivariance and Symmetry Breaking in Convolutional Networks (NeurIPS 2025 · similarity 0.88)
- A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs (ICML 2024 · similarity 0.87)
- Lie Group Decompositions for Equivariant Neural Networks (ICLR 2024 · similarity 0.87)
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