  The problem of Novel Class Discovery (NCD) consists in extracting knowledge
from a labeled set of known classes to accurately partition an unlabeled set of
novel classes. While NCD has recently received a lot of attention from the
community, it is often solved on computer vision problems and under unrealistic
conditions. In particular, the number of novel classes is usually assumed to be
known in advance, and their labels are sometimes used to tune hyperparameters.
Methods that rely on these assumptions are not applicable in real-world
scenarios. In this work, we focus on solving NCD in tabular data when no prior
knowledge of the novel classes is available. To this end, we propose to tune
the hyperparameters of NCD methods by adapting the $k$-fold cross-validation
process and hiding some of the known classes in each fold. Since we have found
that methods with too many hyperparameters are likely to overfit these hidden
classes, we define a simple deep NCD model. This method is composed of only the
essential elements necessary for the NCD problem and performs impressively well
under realistic conditions. Furthermore, we find that the latent space of this
method can be used to reliably estimate the number of novel classes.
Additionally, we adapt two unsupervised clustering algorithms ($k$-means and
Spectral Clustering) to leverage the knowledge of the known classes. Extensive
experiments are conducted on 7 tabular datasets and demonstrate the
effectiveness of the proposed method and hyperparameter tuning process, and
show that the NCD problem can be solved without relying on knowledge from the
novel classes.
