  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.
