Single-source open-domain generalization (SS-ODG) addresses the challenge of
labeled source domains with supervision during training and unlabeled novel
target domains during testing. The target domain includes both known classes
from the source domain and samples from previously unseen classes. Existing
techniques for SS-ODG primarily focus on calibrating source-domain classifiers
to identify open samples in the target domain. However, these methods struggle
with visually fine-grained open-closed data, often misclassifying open samples
as closed-set classes. Moreover, relying solely on a single source domain
restricts the model's ability to generalize. To overcome these limitations, we
propose a novel framework called SODG-Net that simultaneously synthesizes novel
domains and generates pseudo-open samples using a learning-based objective, in
contrast to the ad-hoc mixing strategies commonly found in the literature. Our
approach enhances generalization by diversifying the styles of known class
samples using a novel metric criterion and generates diverse pseudo-open
samples to train a unified and confident multi-class classifier capable of
handling both open and closed-set data. Extensive experimental evaluations
conducted on multiple benchmarks consistently demonstrate the superior
performance of SODG-Net compared to the literature.

单源开放域泛化 (SS-ODG) 通过在训练期间提供带标签的源域和在测试期间提供没有标签的新颖目标域来解决有监督的挑战。现有的技术主要集中于校准源域的分类器以识别目标域中的开放样本。然而，这些方法在对视觉细粒度的开放 - 闭合数据时往往出现错误分类的情况。为了克服这些限制，我们提出了一种名为 SODG-Net 的新颖框架，该框架使用基于学习的目标同时合成新颖域和生成伪开放样本，与文献中常见的临时混合策略形成对比。我们的方法通过使用新颖的度量标准增强了对已知类别样本样式的泛化能力，并生成了多样的伪开放样本，以训练能够处理开放和闭集数据的统一且自信的多类分类器。在多个基准测试中进行的大量实验评估一致表明了 SODG-Net 相对于文献的卓越性能。