Unsupervised Domain Adaptation
State-of-the-art leaderboards
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Greatest papers with code
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
CVPR 2017
• tensorflow/models
• 
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Domain Separation Networks
NeurIPS 2016
• tensorflow/models
•
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
• jindongwang/transferlearning
• 
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
SOTA for Domain Adaptation on ImageCLEF-DA
A Survey of Unsupervised Deep Domain Adaptation
• zhaoxin94/awsome-domain-adaptation
•
Deep learning has produced state-of-the-art results for a variety of tasks.
Correlation Alignment for Unsupervised Domain Adaptation
• eridgd/WCT-TF
•
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.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
CVPR 2018
• mil-tokyo/MCD_DA
•
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.
IMAGE CLASSIFICATION SEMANTIC SEGMENTATION UNSUPERVISED DOMAIN ADAPTATION
Adversarial Discriminative Domain Adaptation
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
OBJECT CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
A DIRT-T Approach to Unsupervised Domain Adaptation
ICLR 2018
• domainadaptation/salad
•
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.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
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.
Learning to cluster in order to transfer across domains and tasks
ICLR 2018
• GT-RIPL/L2C
•
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.