Implicit Equivariance in Convolutional Networks

Understand

Convolutional Neural Networks(CNN) are inherently equivariant under translations, however, they do not have an equivalent embedded mechanism to handle other transformations such as rotations and change in scale.

• Several approaches exist that make CNNs equivariant under other transformation groups by design.
• Among these, steerable CNNs have been especially effective.
• However, these approaches require redesigning standard networks with filters mapped from combinations of predefined basis involving complex analytical functions.