Class-incremental Novel Class Discovery

We study the new task of class-incremental Novel Class Discovery
(class-iNCD), which refers to the problem of discovering novel
categories in an unlabelled data set by leveraging a pre-trained model
that has been trained on a labelled data set containing disjoint yet related
categories. Apart from discovering novel classes, we also aim at preserving
the ability of the model to recognize previously seen base categories.
Inspired by rehearsal-based incremental learning methods, in this paper
we propose a novel approach for class-iNCD which prevents forgetting of
past information about the base classes by jointly exploiting base class
feature prototypes and feature-level knowledge distillation. We also propose
a self-training clustering strategy that simultaneously clusters novel
categories and trains a joint classifier for both the base and novel classes.
This makes our method able to operate in a class-incremental setting.
Our experiments, conducted on three common benchmarks, demonstrate
that our method significantly outperforms state-of-the-art approaches.
Code is available at https://github.com/OatmealLiu/class-iNCD.