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Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Different from existing methods that make label prediction for target samples independently, in this paper, we propose a novel domain adaptation approach that assigns pseudo-labels to target data with the guidance of class centroids in two domains, so that the data distribution structure of both source and target domains can be emphasized.
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
SOTA for Domain Adaptation on ImageCLEF-DA
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.
Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).
#4 best model for Unsupervised Image-To-Image Translation on SVNH-to-MNIST
In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces.
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
#5 best model for Unsupervised Domain Adaptation on Cityscapes to Foggy Cityscapes