Research map / Architectures and efficiency / CNN design
FILTRA: Rethinking Steerable CNN by Filter Transform
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FILTRA instead of R2Conv | classification CNN · MNIST, SO2 augmentation, C8 | accuracy: 98.8 -> 98.9 | Helped |
| Use FILTRA instead of R2Conv | classification CNN · KMNIST, SO2 augmentation, C8 | accuracy: 97.3 -> 97.1 | Hurt |
| Use FILTRA instead of R2Conv | classification CNN · KMNIST, O2 augmentation, D8 | accuracy: 96.8 -> 97.0 | Helped |
| Use FILTRA instead of R2Conv | classification CNN · FashionMNIST | accuracy: 90.5 -> 90.5; 90.8 -> 90.8 | No clear effect |
| Use FILTRA instead of R2Conv | classification CNN · EMNIST | accuracy: 76.7 -> 77.1; 80.1 -> 80.5 | Helped |
| Use FILTRA instead of R2Conv | WideResNet · CIFAR10, SO2 augmentation, C4 | accuracy: 93.6 -> 93.4 | Hurt |
| Use FILTRA instead of R2Conv | WideResNet · CIFAR10, O2 augmentation, D4 | accuracy: 92.7 -> 92.8 | Helped |
| Use FILTRA instead of R2Conv | character-direction regressor · MNIST, SO2 augmentation, C8 | angle error (deg): 4.8 -> 3.3 | Helped |
| Use FILTRA instead of R2Conv | character-direction regressor · EMNIST, O2 augmentation, D8 | angle error (deg): 29.2 -> 24.9 | Helped |
Closest papers by meaning
- General E(2)-Equivariant Steerable CNNs (NeurIPS 2019 · similarity 0.91)
- Steerable Transformers for Volumetric Data (ICML 2025 · similarity 0.88)
- PICNN: A Pathway towards Interpretable Convolutional Neural Networks (AAAI 2024 · similarity 0.85)
- A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs (ICML 2024 · similarity 0.85)
- Clifford-Steerable Convolutional Neural Networks (ICML 2024 · similarity 0.84)
- The Master Key Filters Hypothesis: Deep Filters Are General (AAAI 2025 · similarity 0.84)
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