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.

基于理论分析，我们引入了一个用于提取与基线特征不同的风格信息的独占风格去除模块，从而促进了推理过程，并且易于与其他 NCD 方法集成，作为插件来改善不同分布的新类别上的性能。此外，我们还建立了一个公平的基准，用于未来的 NCD 研究，同时在三个常见数据集上进行的大量实验证明了我们提出的模块的有效性。