Invariance encoding in sliced-Wasserstein space for image classification with limited training data

•We present a mathematical framework to learn invariance to certain image transformations.•The method is computationally efficient, data-efficient, and has no hyper-parameters.•The method is robust where the training and testing distributions are different.•Freely available software implementing the proposed method is provided.