Feature Alignment by Uncertainty and Self-Training for Source-Free
Unsupervised Domain Adaptation
- URL: http://arxiv.org/abs/2208.14888v1
- Date: Wed, 31 Aug 2022 14:28:36 GMT
- Title: Feature Alignment by Uncertainty and Self-Training for Source-Free
Unsupervised Domain Adaptation
- Authors: JoonHo Lee and Gyemin Lee
- Abstract summary: Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation.
We propose a source-free UDA method that uses only a pre-trained source model and unlabeled target images.
Our method captures the aleatoric uncertainty by incorporating data augmentation and trains the feature generator with two consistency objectives.
- Score: 1.6498361958317636
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most unsupervised domain adaptation (UDA) methods assume that labeled source
images are available during model adaptation. However, this assumption is often
infeasible owing to confidentiality issues or memory constraints on mobile
devices. To address these problems, we propose a simple yet effective
source-free UDA method that uses only a pre-trained source model and unlabeled
target images. Our method captures the aleatoric uncertainty by incorporating
data augmentation and trains the feature generator with two consistency
objectives. The feature generator is encouraged to learn consistent visual
features away from the decision boundaries of the head classifier. Inspired by
self-supervised learning, our method promotes inter-space alignment between the
prediction space and the feature space while incorporating intra-space
consistency within the feature space to reduce the domain gap between the
source and target domains. We also consider epistemic uncertainty to boost the
model adaptation performance. Extensive experiments on popular UDA benchmarks
demonstrate that the performance of our approach is comparable or even superior
to vanilla UDA methods without using source images or network modifications.
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