Domain Generalization (DG) aims to resolve distribution shifts between source
and target domains, and current DG methods are default to the setting that data
from source and target domains share identical categories. Nevertheless, there
exists unseen classes from target domains in practical scenarios. To address
this issue, Open Set Domain Generalization (OSDG) has emerged and several
methods have been exclusively proposed. However, most existing methods adopt
complex architectures with slight improvement compared with DG methods.
Recently, vision-language models (VLMs) have been introduced in DG following
the fine-tuning paradigm, but consume huge training overhead with large vision
models. Therefore, in this paper, we innovate to transfer knowledge from VLMs
to lightweight vision models and improve the robustness by introducing
Perturbation Distillation (PD) from three perspectives, including Score, Class
and Instance (SCI), named SCI-PD. Moreover, previous methods are oriented by
the benchmarks with identical and fixed splits, ignoring the divergence between
source domains. These methods are revealed to suffer from sharp performance
decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a
novel metric $H^{2}$-CV, which construct various splits to comprehensively
assess the robustness of algorithms. Extensive experiments demonstrate that our
method outperforms state-of-the-art algorithms on multiple datasets, especially
improving the robustness when confronting data scarcity.

该研究论文介绍了一种通过将知识从轻量级视觉模型转移到视觉 - 语言模型，并引入三个方面的扰动提炼（SCI-PD）来提高鲁棒性的方法，并通过新的混合领域泛化基准和度量进行全面评估，结果表明该方法在多个数据集上优于现有算法，尤其在数据稀缺情况下提高了鲁棒性。

PracticalDG: 视觉 - 语言模型的扰动提取与混合域泛化

PracticalDG: Perturbation Distillation on Vision-Language Models for  Hybrid Domain Generalization

Domain generalization (DG) is proposed to deal with the issue of domain
shift, which occurs when statistical differences exist between source and
target domains. However, most current methods do not account for a common
realistic scenario where the source and target domains have different classes.
To overcome this deficiency, open set domain generalization (OSDG) then emerges
as a more practical setting to recognize unseen classes in unseen domains. An
intuitive approach is to use multiple one-vs-all classifiers to define decision
boundaries for each class and reject the outliers as unknown. However, the
significant class imbalance between positive and negative samples often causes
the boundaries biased towards positive ones, resulting in misclassification for
known samples in the unseen target domain. In this paper, we propose a novel
meta-learning-based framework called dualistic MEta-learning with joint
DomaIn-Class matching (MEDIC), which considers gradient matching towards
inter-domain and inter-class splits simultaneously to find a generalizable
boundary balanced for all tasks. Experimental results demonstrate that MEDIC
not only outperforms previous methods in open set scenarios, but also maintains
competitive close set generalization ability at the same time. Our code is
available at this https URL

本文提出了一种基于元学习的新型框架，即双重 MEta-learning with joint DomaIn-Class matching（MEDIC），该框架同时考虑了领域间和类别间的梯度匹配，以找到适用于所有任务的平衡边界，实验证明该方法不仅在开放集场景中优于以前的方法，同时还保持了有竞争力的闭集泛化能力。