We propose a semantic segmentation model that exploits rotation and
reflection symmetries. We demonstrate significant gains in sample efficiency
due to increased weight sharing, as well as improvements in robustness to
symmetry transformations. The group equivariant CNN framework is extended for
segmentation by introducing a new equivariant (G->Z2)-convolution that
transforms feature maps on a group to planar feature maps. Also, equivariant
transposed convolution is formulated for up-sampling in an encoder-decoder
network. To demonstrate improvements in sample efficiency we evaluate on
multiple data regimes of a rotation-equivariant segmentation task: cancer
metastases detection in histopathology images. We further show the
effectiveness of exploiting more symmetries by varying the size of the group.

我们提出了一种语义分割模型，利用旋转和反射对称性，扩展了等变 CNN 框架，通过引入新的等变（G-》Z2）- 卷积和等变转置卷积，实现了对组别特征图的变换与上采样，进而提高了样本效率和鲁棒性，并在癌症转移组织病理图像的多个数据组织方案的旋转等变分割任务上进行了评估，证明了利用更多对称性的高效性。