Tabular data. Unlike text, audio, and images, classical machine learning tools are
still popular for processing tabular data. However, while deep learning for tabular data
is comparatively a small ﬁeld, ﬁnding sensible data augmentation strategies is already a
much studied topic. Several SSL methods for tabular data utilize masking in various ways
and some techniques creatively employ other augmentations developed for images, like
mixup [Zhang et al., 2018]. As with images and audio, some algorithms aim to generate
the missing or corrupted values while others employ contrastive learning.
A Cookbook of Self-Supervised Learning