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arXiv · 2309.13258

Order-preserving Consistency Regularization for Domain Adaptation and Generalization

Mengmeng Jing·Xiantong Zhen·Jingjing Li·Cees Snoek
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Abstract

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.

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Mengmeng Jing, Xiantong Zhen, Jingjing Li, Cees Snoek. 2023-09-23. Order-preserving Consistency Regularization for Domain Adaptation and Generalization. https://arxiv.org/abs/2309.13258

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