Order-preserving Consistency Regularization for Domain Adaptation and
  Generalization
Mengmeng Jing Xiantong Zhen Jingjing Li Cees Snoek
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.