  As a promising field in open-world learning, \textit{Novel Class Discovery}
(NCD) is usually a task to cluster unseen novel classes in an unlabeled set
based on the prior knowledge of labeled data within the same domain. However,
the performance of existing NCD methods could be severely compromised when
novel classes are sampled from a different distribution with the labeled ones.
In this paper, we explore and establish the solvability of NCD in cross domain
setting with the necessary condition that style information must be removed.
Based on the theoretical analysis, we introduce an exclusive style removal
module for extracting style information that is distinctive from the baseline
features, thereby facilitating inference. Moreover, this module is easy to
integrate with other NCD methods, acting as a plug-in to improve performance on
novel classes with different distributions compared to the seen labeled set.
Additionally, recognizing the non-negligible influence of different backbones
and pre-training strategies on the performance of the NCD methods, we build a
fair benchmark for future NCD research. Extensive experiments on three common
datasets demonstrate the effectiveness of our proposed module.
