Generalized Category Discovery (GCD) aims to classify instances from both known and novel categories within a large-scale unlabeled dataset, a critical yet challenging task for real-world, open-world applications. However, existing methods often rely on pseudo-labeling, or two-stage clustering, which lack a principled mechanism to explicitly disentangle essential, category-defining signals from instance-specific noise. In this paper, we address this fundamental limitation by re-framing GCD from an information-theoretic perspective, grounded in the Information Bottleneck (IB) principle. We introduce InfoSculpt, a novel framework that systematically sculpts the representation space by minimizing a dual Conditional Mutual Information (CMI) objective. InfoSculpt uniquely combines a Category-Level CMI on labeled data to learn compact and discriminative representations for known classes, and a complementary Instance-Level CMI on all data to distill invariant features by compressing augmentation-induced noise. These two objectives work synergistically at different scales to produce a disentangled and robust latent space where categorical information is preserved while noisy, instance-specific details are discarded. Extensive experiments on 8 benchmarks demonstrate that InfoSculpt validating the effectiveness of our information-theoretic approach.

翻译：广义类别发现（GCD）旨在对大规模未标记数据集中的已知类别和新颖类别实例进行分类，这是现实世界开放世界应用中一项关键且具有挑战性的任务。然而，现有方法通常依赖于伪标签或两阶段聚类，缺乏一种原则性机制来显式地从实例特定噪声中解耦出本质的、定义类别的信号。在本文中，我们从信息论视角出发，基于信息瓶颈（IB）原理，重新构建GCD以解决这一根本性局限。我们提出了InfoSculpt，一个通过最小化双重条件互信息（CMI）目标来系统塑造表示空间的新颖框架。InfoSculpt独特地结合了在标注数据上的类别级CMI以学习已知类别的紧凑且具有判别性的表示，以及在所有数据上的互补性实例级CMI，通过压缩由数据增强引入的噪声来提取不变特征。这两个目标在不同尺度上协同工作，产生一个解耦且鲁棒的潜在空间，其中类别信息得以保留，而嘈杂的、实例特定的细节则被摒弃。在8个基准数据集上的大量实验证明了InfoSculpt的有效性，验证了我们信息论方法的优越性。