  Novel class discovery (NCD) aims at learning a model that transfers the
common knowledge from a class-disjoint labelled dataset to another unlabelled
dataset and discovers new classes (clusters) within it. Many methods, as well
as elaborate training pipelines and appropriate objectives, have been proposed
and considerably boosted performance on NCD tasks. Despite all this, we find
that the existing methods do not sufficiently take advantage of the essence of
the NCD setting. To this end, in this paper, we propose to model both
inter-class and intra-class constraints in NCD based on the symmetric
Kullback-Leibler divergence (sKLD). Specifically, we propose an inter-class
sKLD constraint to effectively exploit the disjoint relationship between
labelled and unlabelled classes, enforcing the separability for different
classes in the embedding space. In addition, we present an intra-class sKLD
constraint to explicitly constrain the intra-relationship between a sample and
its augmentations and ensure the stability of the training process at the same
time. We conduct extensive experiments on the popular CIFAR10, CIFAR100 and
ImageNet benchmarks and successfully demonstrate that our method can establish
a new state of the art and can achieve significant performance improvements,
e.g., 3.5%/3.7% clustering accuracy improvements on CIFAR100-50 dataset split
under the task-aware/-agnostic evaluation protocol, over previous
state-of-the-art methods. Code is available at
https://github.com/FanZhichen/NCD-IIC.
