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 \textbf{9.7}$\%$ within the Stanford Cars dataset and
\textbf{12$\times$} clustering efficiency within the Herbarium 19 dataset. We
will make the code and checkpoints publicly available at
https://github.com/xjtuYW/PNP.git.

基于知识学习的通用类发现 (GCD) 旨在根据来自已标记和未标记数据的知识动态地为未标记数据分配标签，然而现有方法主要依赖于聚类算法的性能，并受限于聚类算法的固有局限性。

超越已知聚类：为高效泛化类别发现探索新原型

Beyond Known Clusters: Probe New Prototypes for Efficient Generalized  Class Discovery

Generalized class discovery (GCD) aims to infer known and unknown categories
in an unlabeled dataset leveraging prior knowledge of a labeled set comprising
known classes. Existing research implicitly/explicitly assumes that the
frequency of occurrence for each category, whether known or unknown, is
approximately the same in the unlabeled data. However, in nature, we are more
likely to encounter known/common classes than unknown/uncommon ones, according
to the long-tailed property of visual classes. Therefore, we present a
challenging and practical problem, Imbalanced Generalized Category Discovery
(ImbaGCD), where the distribution of unlabeled data is imbalanced, with known
classes being more frequent than unknown ones. To address these issues, we
propose ImbaGCD, A novel optimal transport-based expectation maximization
framework that accomplishes generalized category discovery by aligning the
marginal class prior distribution. ImbaGCD also incorporates a systematic
mechanism for estimating the imbalanced class prior distribution under the GCD
setup. Our comprehensive experiments reveal that ImbaGCD surpasses previous
state-of-the-art GCD methods by achieving an improvement of approximately 2 -
4% on CIFAR-100 and 15 - 19% on ImageNet-100, indicating its superior
effectiveness in solving the Imbalanced GCD problem.

不平衡的广义类别发现（ImbaGCD）是一个具有挑战性且实用的问题，该问题在未标记的数据集中，已知类别的分布偏向于常见类别而不是罕见类别。为了解决这些问题，我们提出了一种新的基于最优传输的期望最大化框架（ImbaGCD），通过对齐边际类别先验分布来完成广义类别发现，并在 GCD 设置下结合了一种估计不平衡类别先验分布的系统机制。我们的全面实验表明，ImbaGCD 在 CIFAR-100 上改进了约 2-4％，在 ImageNet-100 上改进了 15-19％，表明其在解决不平衡 GCD 问题方面具有优越的效果。

ImbaGCD: 不平衡广义类别发现

ImbaGCD: Imbalanced Generalized Category Discovery

Generalized Class Discovery (GCD) plays a pivotal role in discerning both
known and unknown categories from unlabeled datasets by harnessing the insights
derived from a labeled set comprising recognized classes. A significant
limitation in prevailing GCD methods is their presumption of an equitably
distributed category occurrence in unlabeled data. Contrary to this assumption,
visual classes in natural environments typically exhibit a long-tailed
distribution, with known or prevalent categories surfacing more frequently than
their rarer counterparts. Our research endeavors to bridge this disconnect by
focusing on the long-tailed Generalized Category Discovery (Long-tailed GCD)
paradigm, which echoes the innate imbalances of real-world unlabeled datasets.
In response to the unique challenges posed by Long-tailed GCD, we present a
robust methodology anchored in two strategic regularizations: (i) a reweighting
mechanism that bolsters the prominence of less-represented, tail-end
categories, and (ii) a class prior constraint that aligns with the anticipated
class distribution. Comprehensive experiments reveal that our proposed method
surpasses previous state-of-the-art GCD methods by achieving an improvement of
approximately 6 - 9% on ImageNet100 and competitive performance on CIFAR100.

通过研究基于长尾分布的广义类别发现 (Long-tailed GCD) 范式，以及支持稀有类别和预期类别分布的重新加权机制和类先验约束，我们提出的方法在 ImageNet100 上实现了约 6-9% 的改善，并在 CIFAR100 上取得了有竞争力的性能。