Papers › Group Equivariant Convolutional Networks
Group Equivariant Convolutional Networks
Taco S. Cohen, Max Welling
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers. G-convolutions increase the expressive capacity of the network without increasing the number of parameters. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reflections and rotations. G-CNNs achieve state of the art results on CIFAR10 and rotated MNIST.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Breast Tumour Classification | PCam | G-CNN (C4) | AUC | 0.964 | #6 of 16 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | G-CNN (C4) | Dice | 0.856 | #8 of 15 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | G-CNN (C4) | F1-score | 0.833 | #8 of 15 | Archive leaderboard | report |
| Colorectal Gland Segmentation: | CRAG | G-CNN (C4) | Hausdorff Distance (mm) | 170.4 | #8 of 15 | Archive leaderboard | report |
| Multi-tissue Nucleus Segmentation | Kumar | G-CNN (C4) | Dice | 0.793 | #15 of 18 | Archive leaderboard | report |
| Multi-tissue Nucleus Segmentation | Kumar | G-CNN (C4) | Hausdorff Distance (mm) | 49.0 | #15 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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