Source-Free Domain Adaptation via Distribution Estimation
- URL: http://arxiv.org/abs/2204.11257v1
- Date: Sun, 24 Apr 2022 12:22:19 GMT
- Title: Source-Free Domain Adaptation via Distribution Estimation
- Authors: Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, Dacheng Tao
- Abstract summary: Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.
Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data.
In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
- Score: 106.48277721860036
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Domain Adaptation aims to transfer the knowledge learned from a labeled
source domain to an unlabeled target domain whose data distributions are
different. However, the training data in source domain required by most of the
existing methods is usually unavailable in real-world applications due to
privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has
drawn much attention, which tries to tackle domain adaptation problem without
using source data. In this work, we propose a novel framework called SFDA-DE to
address SFDA task via source Distribution Estimation. Firstly, we produce
robust pseudo-labels for target data with spherical k-means clustering, whose
initial class centers are the weight vectors (anchors) learned by the
classifier of pretrained model. Furthermore, we propose to estimate the
class-conditioned feature distribution of source domain by exploiting target
data and corresponding anchors. Finally, we sample surrogate features from the
estimated distribution, which are then utilized to align two domains by
minimizing a contrastive adaptation loss function. Extensive experiments show
that the proposed method achieves state-of-the-art performance on multiple DA
benchmarks, and even outperforms traditional DA methods which require plenty of
source data.
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