Unsupervised Domain Adaptation
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
Learning Generalisable Omni-Scale Representations for Person Re-Identification
• KaiyangZhou/deep-person-reid
•
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.
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.
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
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.
Unsupervised Domain Adaptation by Backpropagation
• fungtion/DANN
•
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
IMAGE CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
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
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.