# Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks Source: [Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:07:03.168Z Authors: Jasper Linmans, Jim Winkens, Bastiaan S. Veeling, Taco S. Cohen, Max Welling Published: 2018-07-02T10:31:05.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()