2026
Soft Equivariance Regularization for Invariant Self-Supervised Learning
ICLR 2026poster
A central principle in self-supervised learning (SSL) is to learn data representations that are invariant to semantic-preserving transformations \eg, image representations should remain unchanged under augmentations like cropping or color jitter. While effective for classification, such invariance c…