Group Equivariant Convolutional Networks

24 Feb 2016Taco S. CohenMax 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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Colorectal Gland Segmentation: CRAG G-CNN (C4) F1-score 0.833 # 6
Dice 0.856 # 7
Hausdorff Distance (mm) 170.4 # 8
Multi-tissue Nucleus Segmentation Kumar G-CNN (C4) Dice 0.793 # 11
Hausdorff Distance (mm) 49.0 # 14
Breast Tumour Classification PCam G-CNN (C4) AUC 0.964 # 5

Methods used in the Paper