In many machine learning applications, the so called data augmentation methods have allowed building better models. For example, assume a training set of $100$ images of cats and dogs. By rotating, mirroring, adjusting contrast, etc. it is possible to generate additional images from the original ones.
In the case of images, the data augmentation is relatively straightforward. However, suppose (for example) that one has a training set of $100$ samples and few hundred continuous variables that represent different things. The data augmentation does not anymore seem so intuitive. What could be done in such case?