Transfering Low-Frequency Features for Domain Adaptation
- URL: http://arxiv.org/abs/2208.14706v1
- Date: Wed, 31 Aug 2022 09:13:25 GMT
- Title: Transfering Low-Frequency Features for Domain Adaptation
- Authors: Zhaowen Li, Xu Zhao, Chaoyang Zhao, Ming Tang and Jinqiao Wang
- Abstract summary: We introduce an approach, named low-frequency module (LFM), to extract domain-invariant feature representations.
Experimental results demonstrate that our LFM outperforms state-of-the-art methods for various computer vision tasks.
- Score: 44.86474562827323
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 proposes the
assumption that low-frequency information is more domain-invariant while the
high-frequency information contains 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 hyperparameter. 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 methods for various computer vision tasks, including image
classification and object detection.
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