Multi-Source Unsupervised Domain Adaptation

20 papers with code • 9 benchmarks • 5 datasets

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Use these libraries to find Multi-Source Unsupervised Domain Adaptation models and implementations

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.

Learning Transferable Features with Deep Adaptation Networks

thuml/Transfer-Learning-Library 10 Feb 2015

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.

Deep Transfer Learning with Joint Adaptation Networks

kevinmusgrave/pytorch-adapt 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.

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift


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 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.