  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.
