In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer Across Domains) as a remedy. CDAD-NET is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets, while emphasizing the distinct categorization of the target data. To facilitate this, we propose an entropy-driven adversarial learning strategy that accounts for the distance distributions of target samples relative to source-domain class prototypes. Parallelly, the discriminative nature of the shared space is upheld through a fusion of three metric learning objectives. In the source domain, our focus is on refining the proximity between samples and their affiliated class prototypes, while in the target domain, we integrate a neighborhood-centric contrastive learning mechanism, enriched with an adept neighborsmining approach. To further accentuate the nuanced feature interrelation among semantically aligned images, we champion the concept of conditional image inpainting, underscoring the premise that semantically analogous images prove more efficacious to the task than their disjointed counterparts. Experimentally, CDAD-NET eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.

翻译：在广义类别发现（GCD）中，我们需对已知和未知类别的未标注样本进行聚类，并利用已知类别的训练数据集。由于数据集之间存在领域偏移，一个显著的挑战随之产生。为解决此问题，我们提出一种新场景：跨领域广义类别发现（AD-GCD），并设计了CDAD-NET（跨领域类别发现器）作为应对方案。CDAD-NET旨在协调标注（源域）和未标注（目标域）数据集中潜在的已知类别样本，同时强调对目标数据的独特分类。为此，我们提出一种熵驱动的对抗学习策略，该策略考量目标样本相对于源域类别原型的距离分布。与此同时，通过融合三种度量学习目标，维护共享空间的判别性特征。在源域中，我们专注于优化样本与其所属类别原型之间的邻近性；而在目标域中，我们集成一种邻域中心对比学习机制，并辅以高效的邻居挖掘方法。为进一步凸显语义对齐图像间细微的特征关联，我们倡导条件图像补全的概念，强调语义相似的图像比无关联图像对任务更有效。实验表明，在我们提出的三个AD-GCD基准测试中，CDAD-NET性能较现有文献提升8-15%。