  Recent literature in self-supervised has demonstrated significant progress in
closing the gap between supervised and unsupervised methods in the image and
text domains. These methods rely on domain-specific augmentations that are not
directly amenable to the tabular domain. Instead, we introduce Contrastive
Mixup, a semi-supervised learning framework for tabular data and demonstrate
its effectiveness in limited annotated data settings. Our proposed method
leverages Mixup-based augmentation under the manifold assumption by mapping
samples to a low dimensional latent space and encourage interpolated samples to
have high a similarity within the same labeled class. Unlabeled samples are
additionally employed via a transductive label propagation method to further
enrich the set of similar and dissimilar pairs that can be used in the
contrastive loss term. We demonstrate the effectiveness of the proposed
framework on public tabular datasets and real-world clinical datasets.
