Multi-Source Unsupervised Domain Adaptation
24 papers with code • 9 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Source Unsupervised Domain Adaptation
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Libraries
Use these libraries to find Multi-Source Unsupervised Domain Adaptation models and implementations
Office-Home
DomainNet
Office-31
GTZAN
Most implemented papers
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.
Learning Transferable Features with Deep Adaptation Networks
thuml/Transfer-Learning-Library
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Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.
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.
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.
Deep Transfer Learning with Joint Adaptation Networks
thuml/Transfer-Learning-Library
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ICML 2017
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain.
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
HCPLab-SYSU/MSDA
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CVPR 2018
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-source Distilling Domain Adaptation
daoyuan98/MDDA
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Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
Domain Adaptive Ensemble Learning
Each such classifier is an expert to its own domain and a non-expert to others.
Multi-source Attention for Unsupervised Domain Adaptation
summer1278/multi-source-attention
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Asian Chapter of the Association for Computational Linguistics 2020
We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance.
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
ChrisAllenMing/LtC-MSDA
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ECCV 2020
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation.
