Related papers: Order-preserving Consistency Regularization for Domain Adaptation and Generalization

Order-preserving Consistency Regularization for Domain Adaptation and Generalization

URL: http://arxiv.org/abs/2309.13258v1
Date: Sat, 23 Sep 2023 04:45:42 GMT
Title: Order-preserving Consistency Regularization for Domain Adaptation and Generalization
Authors: Mengmeng Jing, Xiantong Zhen, Jingjing Li, Cees Snoek
Abstract summary: Deep learning models fail on cross-domain challenges if the model is oversensitive to domain-specific attributes. We propose the Order-preserving Consistency Regularization (OCR) for cross-domain tasks.
Score: 45.64969000499267
License: http://creativecommons.org/licenses/by/4.0/
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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