  Generalized Class Discovery (GCD) aims to dynamically assign labels to
unlabelled data partially based on knowledge learned from labelled data, where
the unlabelled data may come from known or novel classes. The prevailing
approach generally involves clustering across all data and learning conceptions
by prototypical contrastive learning. However, existing methods largely hinge
on the performance of clustering algorithms and are thus subject to their
inherent limitations. Firstly, the estimated cluster number is often smaller
than the ground truth, making the existing methods suffer from the lack of
prototypes for comprehensive conception learning. To address this issue, we
propose an adaptive probing mechanism that introduces learnable potential
prototypes to expand cluster prototypes (centers). As there is no ground truth
for the potential prototype, we develop a self-supervised prototype learning
framework to optimize the potential prototype in an end-to-end fashion.
Secondly, clustering is computationally intensive, and the conventional
strategy of clustering both labelled and unlabelled instances exacerbates this
issue. To counteract this inefficiency, we opt to cluster only the unlabelled
instances and subsequently expand the cluster prototypes with our introduced
potential prototypes to fast explore novel classes. Despite the simplicity of
our proposed method, extensive empirical analysis on a wide range of datasets
confirms that our method consistently delivers state-of-the-art results.
Specifically, our method surpasses the nearest competitor by a significant
margin of 9.7% within the Stanford Cars dataset and 12x clustering efficiency
within the Herbarium 19 dataset. We will make the code and checkpoints publicly
available at https://github.com/xjtuYW/PNP.git.
