  In Novel Class Discovery (NCD), the goal is to find new classes in an
unlabeled set given a labeled set of known but different classes. While NCD has
recently gained attention from the community, no framework has yet been
proposed for heterogeneous tabular data, despite being a very common
representation of data. In this paper, we propose TabularNCD, a new method for
discovering novel classes in tabular data. We show a way to extract knowledge
from already known classes to guide the discovery process of novel classes in
the context of tabular data which contains heterogeneous variables. A part of
this process is done by a new method for defining pseudo labels, and we follow
recent findings in Multi-Task Learning to optimize a joint objective function.
Our method demonstrates that NCD is not only applicable to images but also to
heterogeneous tabular data. Extensive experiments are conducted to evaluate our
method and demonstrate its effectiveness against 3 competitors on 7 diverse
public classification datasets.
