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Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
SOTA for Domain Adaptation on ImageCLEF-DA
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.
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.
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
#3 best model for Unsupervised Image-To-Image Translation on SVNH-to-MNIST
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation.