Achieving high accuracy on data from domains unseen during training is a fundamental challenge in domain generalization (DG). While state-of-the-art DG classifiers have demonstrated impressive performance across various tasks, they have shown a bias towards domain-dependent information, such as image styles, rather than domain-invariant information, such as image content. This bias renders them unreliable for deployment in risk-sensitive scenarios such as autonomous driving where a misclassification could lead to catastrophic consequences. To enable risk-averse predictions from a DG classifier, we propose a novel inference procedure, Test-Time Neural Style Smoothing (TT-NSS), that uses a "style-smoothed" version of the DG classifier for prediction at test time. Specifically, the style-smoothed classifier classifies a test image as the most probable class predicted by the DG classifier on random re-stylizations of the test image. TT-NSS uses a neural style transfer module to stylize a test image on the fly, requires only black-box access to the DG classifier, and crucially, abstains when predictions of the DG classifier on the stylized test images lack consensus. Additionally, we propose a neural style smoothing (NSS) based training procedure that can be seamlessly integrated with existing DG methods. This procedure enhances prediction consistency, improving the performance of TT-NSS on non-abstained samples. Our empirical results demonstrate the effectiveness of TT-NSS and NSS at producing and improving risk-averse predictions on unseen domains from DG classifiers trained with SOTA training methods on various benchmark datasets and their variations.

翻译：在领域泛化（DG）中，对训练期间未见过的数据域实现高精度是一项基本挑战。尽管最先进的DG分类器在多项任务中展现出卓越性能，但它们倾向于依赖与领域相关的信息（如图像风格），而非与领域无关的信息（如图像内容）。这种偏差使得它们在风险敏感场景（如自动驾驶，其中误分类可能导致灾难性后果）中不可靠。为赋予DG分类器风险规避的预测能力，我们提出一种新型推理流程——测试时神经风格平滑（TT-NSS），该方法在测试时使用DG分类器的“风格平滑”版本进行预测。具体而言，风格平滑分类器将测试图像归类为DG分类器对该测试图像随机重风格化版本预测的最可能类别。TT-NSS通过神经风格迁移模块实时对测试图像进行风格化处理，仅需对DG分类器的黑盒访问权限，关键是在DG分类器对风格化测试图像的预测缺乏共识时主动弃权。此外，我们提出一种基于神经风格平滑（NSS）的训练流程，可无缝集成至现有DG方法中。该流程增强预测一致性，从而提升TT-NSS对非弃权样本的性能。实验结果表明，TT-NSS和NSS能有效生成并改进来自SOTA训练方法训练的DG分类器在各类基准数据集及其变体上对未见域的风险规避预测。