However, the main benefit is the consistency in output despite any transformations to the input -without SE(3)-equivariance, data augmentation would be required to learn this same phenomenon, leading to significantly increased training times. Some representations avoid the need for SE(3)-equivariance by using relative information to create invariant representations. A function, 𝑓(𝑥), is invariant under a transformation, 𝑡(𝑥), if the output remains unchanged when the transformation is applied: