  Self-supervised learning converts raw perceptual data such as images to a
compact space where simple Euclidean distances measure meaningful variations in
data. In this paper, we extend this formulation by adding additional geometric
structure to the embedding space by enforcing transformations of input space to
correspond to simple (i.e., linear) transformations of embedding space.
Specifically, in the contrastive learning setting, we introduce an equivariance
objective and theoretically prove that its minima forces augmentations on input
space to correspond to rotations on the spherical embedding space. We show that
merely combining our equivariant loss with a non-collapse term results in
non-trivial representations, without requiring invariance to data
augmentations. Optimal performance is achieved by also encouraging approximate
invariance, where input augmentations correspond to small rotations. Our
method, CARE: Contrastive Augmentation-induced Rotational Equivariance, leads
to improved performance on downstream tasks, and ensures sensitivity in
embedding space to important variations in data (e.g., color) that standard
contrastive methods do not achieve. Code is available at
https://github.com/Sharut/CARE.
