Deep neural networks (DNNs) usually fail to generalize well to outside of
distribution (OOD) data, especially in the extreme case of single domain
generalization (single-DG) that transfers DNNs from single domain to multiple
unseen domains. Existing single-DG techniques commonly devise various
data-augmentation algorithms, and remould the multi-source domain
generalization methodology to learn domain-generalized (semantic) features.
Nevertheless, these methods are typically modality-specific, thereby being only
applicable to one single modality (e.g., image). In contrast, we target a
versatile Modality-Agnostic Debiasing (MAD) framework for single-DG, that
enables generalization for different modalities. Technically, MAD introduces a
novel two-branch classifier: a biased-branch encourages the classifier to
identify the domain-specific (superficial) features, and a general-branch
captures domain-generalized features based on the knowledge from biased-branch.
Our MAD is appealing in view that it is pluggable to most single-DG models. We
validate the superiority of our MAD in a variety of single-DG scenarios with
different modalities, including recognition on 1D texts, 2D images, 3D point
clouds, and semantic segmentation on 2D images. More remarkably, for
recognition on 3D point clouds and semantic segmentation on 2D images, MAD
improves DSU by 2.82\% and 1.5\% in accuracy and mIOU.

提出了一种适用于多种不同模态的深度神经网络的单一领域通用性（single-DG）算法，称之为 Modality-Agnostic Debiasing (MAD)，它采用一个新颖的双分支分类器，一支专注于识别特定领域的表层特征，另一个则利用来自前一支的知识来捕获领域通用（语义）特征，在多种领域通用任务中取得了极高的效果。