  Recent semi-supervised and self-supervised methods have shown great success
in the image and text domain by utilizing augmentation techniques. Despite such
success, it is not easy to transfer this success to tabular domains. It is not
easy to adapt domain-specific transformations from image and language to
tabular data due to mixing of different data types (continuous data and
categorical data) in the tabular domain. There are a few semi-supervised works
on the tabular domain that have focused on proposing new augmentation
techniques for tabular data. These approaches may have shown some improvement
on datasets with low-cardinality in categorical data. However, the fundamental
challenges have not been tackled. The proposed methods either do not apply to
datasets with high-cardinality or do not use an efficient encoding of
categorical data. We propose using conditional probability representation and
an efficient progressively feature upgrading framework to effectively learn
representations for tabular data in semi-supervised applications. The extensive
experiments show superior performance of the proposed framework and the
potential application in semi-supervised settings.
