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.
翻译:深心神经网络(DNNs)通常无法向分布(OOD)数据外的分布式(OOD)数据全面推广,特别是在将DNN从单一域传输到多个未知域的单一域通用(单一DG)的极端情况下。现有的单DT技术通常设计各种数据增强算法,并重塑多源域通用方法,以学习域通用(静态)特征。然而,这些方法一般是模式特有的,因此只适用于单一模式(如图像)。相比之下,我们针对单一DG的多功能模式-Agnotiscal化(单一D)框架(单一DDD),使DNNNP从单一域向多个未知域传输。在技术上,MAD引入了一种新型的双分级分类:偏向分级鼓励分类者识别域特定(超度)特征,以及基于偏见组合知识的通用分域域特征。我们的MAD认为它可以连接到大多数单DDDD 3级图象模型,在单DD2级图解中,在SOD2级图象模型中,我们对单DMD2级的高级分级图象的分级的分数的分级识别。</s>