PracticalDG: Perturbation Distillation on Vision-Language Models for
  Hybrid Domain Generalization
Zining Chen Weiqiu Wang Zhicheng Zhao Fei Su Aidong Men Hongying Meng
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.