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
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Semantic segmentation is a key problem for many computer vision tasks.
Virtual Mixup Training for Unsupervised Domain Adaptation
xudonmao/VMT
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Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
microsoft/S2R-DepthNet
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CVPR 2021
S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation.
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping.
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Moment Matching for Multi-Source Domain Adaptation
lavoiems/Cats-UDT
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ICCV 2019
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
Unsupervised Domain Adaptation through Self-Supervision
yueatsprograms/uda_release
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This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
