Unsupervised Domain Adaptation
730 papers with code • 36 benchmarks • 31 datasets
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Benchmarks
These leaderboards are used to track progress in Unsupervised Domain Adaptation
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Libraries
Use these libraries to find Unsupervised Domain Adaptation models and implementationsDatasets
Office-Home
Market-1501
DomainNet
Office-31
PACS
ImageNet-C
SYNTHIA
GTA5
ImageNet-R
ImageNet-A
Most implemented papers
Domain-Adversarial Training of Neural Networks
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
yxgeee/MMT
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CVPR 2018
Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e. g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network.
Unsupervised Domain Adaptation by Backpropagation
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).
Adversarial Discriminative Domain Adaptation
thuml/Transfer-Learning-Library
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CVPR 2017
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
Joint Discriminative and Generative Learning for Person Re-identification
layumi/Person_reID_baseline_pytorch
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CVPR 2019
To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
thuml/Transfer-Learning-Library
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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.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
mil-tokyo/MCD_DA
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CVPR 2018
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.
Domain Adaptive Faster R-CNN for Object Detection in the Wild
yuhuayc/da-faster-rcnn
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CVPR 2018
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
Learning Generalisable Omni-Scale Representations for Person Re-Identification
KaiyangZhou/deep-person-reid
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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.
Deep Hashing Network for Unsupervised Domain Adaptation
Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain.
