We tackle the novel class discovery problem, aiming to discover novel classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge contained in the seen classes to unseen ones. Previous methods mostly transfer knowledge through sharing representation space or joint label space. However, they tend to neglect the class relation between seen and unseen categories, and thus the learned representations are less effective for clustering unseen classes. In this paper, we propose a principle and general method to transfer semantic knowledge between seen and unseen classes. Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge. To validate the effectiveness and generalization of our method, we conduct extensive experiments both on novel class discovery and general novel class discovery settings. Our results show that the proposed method outperforms previous SOTA by a significant margin on several benchmarks.

翻译：我们处理新颖类类发现问题,目的是根据有标签的分类数据发现无标签数据中的新类,主要挑战在于将可见类中包含的知识转让给看不见类。过去的方法主要是通过共享代表空间或共同标签空间转让知识。然而,它们往往忽视了被看见类别和不可见类别之间的类关系,因此,所学的表述对于将不可见类分组不那么有效。在本文中,我们提出了一个原则和一般方法,在被看见类和不可见类之间转让语义知识。我们的洞察力是利用相互信息测量在受限制的标签空间中被看见的类和不可见类之间的关系,最大限度地利用相互信息促进传播语义知识。为了验证我们方法的有效性和普遍性,我们进行了关于新类发现和一般新类发现环境的广泛实验。我们的结果显示,拟议的方法在几个基准上大大超越了前SOTA。