Unsupervised Domain Adaptation
477 papers with code • 23 benchmarks • 22 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
ImageNet
KITTI
Market-1501
Office-Home
Office-31
SYNTHIA
GTA5
ImageNet-C
VisDA-2017
Foggy Cityscapes
Most implemented papers
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
damo-cv/transreid
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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.
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.
Rescaling Egocentric Vision
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
tensorflow/models
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CVPR 2017
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Domain Separation Networks
tensorflow/models
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NeurIPS 2016
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
google-research/adamatch
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ICLR 2022
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
