Mutual Information-guided Knowledge Transfer for Novel Class Discovery

Understand

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.