Multi-Source Unsupervised Domain Adaptation
15 papers with code • 7 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Source Unsupervised Domain Adaptation
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Office-Home
Office-31
DomainNet
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.
Deep Transfer Learning with Joint Adaptation Networks
criteo-research/pytorch-ada
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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.
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 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.
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
FengHZ/KD3A
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(2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
anh-ntv/STEM_iccv21
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ICCV 2021
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
tuanrpt/MOST
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UAI 2021
To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
