The Norm Must Go On - Dynamic Unsupervised Domain Adaptation by Normalization.

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e.g. autonomous driving in challenging weather conditions. To address this problem of continuous adaptation to distribution shifts, we propose Dynamic Unsupervised Adaptation (DUA). We modify the feature representations of the model by continuously adapting the statistics of the batch normalization layers. We show that by accessing only a tiny fraction of unlabeled data from the shifted domain and adapting sequentially, a strong performance gain can be achieved. With even less than 1% of unlabeled data from the target domain, DUA already achieves competitive results to strong baselines. In addition, the computational overhead is minimal in contrast to previous approaches. Our approach is simple, yet effective and can be applied to any architecture which uses batch normalization as one of its components. We show the utility of DUA by evaluating it on a variety of domain adaptation datasets and tasks including object recognition, digit recognition and object detection
Original languageEnglish
Title of host publicationIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Number of pages17
Publication statusPublished - 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2022 - New Orleans Ernest N. Morial Convention Center, Hybrider Event, New Orleans, United States
Duration: 21 Jun 202224 Sept 2022
Conference number: 2022


Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2022
Country/TerritoryUnited States
CityHybrider Event, New Orleans

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