  While the novel class discovery has recently made great progress, existing
methods typically focus on improving algorithms on class-balanced benchmarks.
However, in real-world recognition tasks, the class distributions of their
corresponding datasets are often imbalanced, which leads to serious performance
degeneration of those methods. In this paper, we consider a more realistic
setting for novel class discovery where the distributions of novel and known
classes are long-tailed. One main challenge of this new problem is to discover
imbalanced novel classes with the help of long-tailed known classes. To tackle
this problem, we propose an adaptive self-labeling strategy based on an
equiangular prototype representation of classes. Our method infers high-quality
pseudo-labels for the novel classes by solving a relaxed optimal transport
problem and effectively mitigates the class biases in learning the known and
novel classes. We perform extensive experiments on CIFAR100, ImageNet100,
Herbarium19 and large-scale iNaturalist18 datasets, and the results demonstrate
the superiority of our method. Our code is available at
https://github.com/kleinzcy/NCDLR.
