  Novel Class Discovery (NCD) is a learning paradigm, where a machine learning
model is tasked to semantically group instances from unlabeled data, by
utilizing labeled instances from a disjoint set of classes. In this work, we
first characterize existing NCD approaches into single-stage and two-stage
methods based on whether they require access to labeled and unlabeled data
together while discovering new classes. Next, we devise a simple yet powerful
loss function that enforces separability in the latent space using cues from
multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed
formulation can either operate as a standalone method or can be plugged into
existing methods to enhance them. We validate the efficacy of Spacing Loss with
thorough experimental evaluation across multiple settings on CIFAR-10 and
CIFAR-100 datasets.
