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- W4293518563 abstract "Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision. The images or feature maps of different domains can be decomposed into the low-frequency component and high-frequency component. This paper pro-poses the assumption that low-frequency information is more domain-invariant while the high-frequency information con-tains domain-related information. Hence, we introduce an approach, named low-frequency module (LFM), to extract domain-invariant feature representations. The LFM is constructed with the digital Gaussian low-pass filter. Our method is easy to implement and introduces no extra hyperparame-ter. We design two effective ways to utilize the LFM for domain adaptation, and our method is complementary to other existing methods and formulated as a plug-and-play unit that can be combined with these methods. Experimental results demonstrate that our LFM outperforms state-of-the-art meth-ods for various computer vision tasks, including image clas-sification and object detection." @default.
- W4293518563 created "2022-08-30" @default.
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- W4293518563 date "2022-07-18" @default.
- W4293518563 modified "2023-10-16" @default.
- W4293518563 title "Transfering Low-Frequency Features for Domain Adaptation" @default.
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- W4293518563 doi "https://doi.org/10.1109/icme52920.2022.9859655" @default.
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