Deep learning models fail on cross-domain challenges if the model is
oversensitive to domain-specific attributes, e.g., lightning, background,
camera angle, etc. To alleviate this problem, data augmentation coupled with
consistency regularization are commonly adopted to make the model less
sensitive to domain-specific attributes. Consistency regularization enforces
the model to output the same representation or prediction for two views of one
image. These constraints, however, are either too strict or not
order-preserving for the classification probabilities. In this work, we propose
the Order-preserving Consistency Regularization (OCR) for cross-domain tasks.
The order-preserving property for the prediction makes the model robust to
task-irrelevant transformations. As a result, the model becomes less sensitive
to the domain-specific attributes. The comprehensive experiments show that our
method achieves clear advantages on five different cross-domain tasks.

为了解决深度学习模型在跨领域挑战中对特定领域属性过于敏感的问题，本文提出了一种基于次序保持一致性规则（OCR）的方法，通过保持预测的次序性，使模型对任务无关的转换具有鲁棒性，从而降低对特定领域属性的敏感性。在五个不同的跨领域任务上，该方法显示出明显优势。