Equivariant Representation Learning via Class-Pose Decomposition

Understand

We introduce a general method for learning representations that are equivariant to symmetries of data.

• Our central idea is to decompose the latent space into an invariant factor and the symmetry group itself.
• The components semantically correspond to intrinsic data classes and poses respectively.
• The learner is trained on a loss encouraging equivariance based on supervision from relative symmetry information.