Learning Steerable Filters for Rotation Equivariant CNNs

  title={Learning Steerable Filters for Rotation Equivariant CNNs},
  author={Maurice Weiler and Fred A. Hamprecht and Martin Storath},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equivariance under translations and rotations by design. The proposed architecture employs steerable… 

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