Multi-Source Unsupervised Domain Adaptation

15 papers with code • 7 benchmarks • 3 datasets

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Most implemented papers

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

mil-tokyo/MCD_DA 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 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 ICCV 2019

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.

Multi-source Distilling Domain Adaptation

daoyuan98/MDDA 22 Nov 2019

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

KaiyangZhou/Dassl.pytorch 16 Mar 2020

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 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 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 19 Nov 2020

(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 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 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.