Exploiting Cyclic Symmetry in Convolutional Neural Networks

Understand

Many classes of images exhibit rotational symmetry.

• Convolutional neural networks are sometimes trained using data augmentation to exploit this, but they are still required to learn the rotation equivariance properties from the data.
• Encoding these properties into the network architecture, as we are already used to doing for translation equivariance by using convolutional layers, could result in a more efficient use of the parameter budget by relieving the model from learning them.
• We introduce four operations which can be inserted into neural network models as layers, and which can be combined to make these models partially equivariant to rotations.