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The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization
[article]
2022
arXiv
pre-print
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
arXiv:2112.00463v2
fatcat:du76jsbx5zb5vonjabbeaaslpa