Domain Adaptation

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2901–2925 of 6439 papers

Title	Date	Tasks	Status
EMN: Brain-inspired Elastic Memory Network for Quick Domain Adaptive Feature Mapping	Feb 4, 2024	Domain AdaptationMemorization	—Unverified
Dependency Parsing: Past, Present, and Future	Aug 1, 2014	Dependency ParsingDomain Adaptation	—Unverified
Dependency Parsing Domain Adaptation using Transductive SVM	Apr 1, 2012	Dependency ParsingDomain Adaptation	—Unverified
BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective	Feb 19, 2022	Domain AdaptationMetric Learning	—Unverified
Dependency Parser Adaptation with Subtrees from Auto-Parsed Target Domain Data	Aug 1, 2013	Constituency ParsingDependency Parsing	—Unverified
Dependable Neural Networks for Safety Critical Tasks	Dec 20, 2019	Autonomous VehiclesDomain Adaptation	—Unverified
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision	Aug 19, 2021	Domain AdaptationImage Segmentation	—Unverified
A Multi-Platform Annotation Ecosystem for Domain Adaptation	Aug 1, 2019	Domain Adaptationtext annotation	—Unverified
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach	Dec 9, 2021	Domain AdaptationStereo Matching	—Unverified
Active Sentiment Domain Adaptation	Jul 1, 2017	Active LearningDomain Adaptation	—Unverified
A Brief Review of Domain Adaptation	Oct 7, 2020	BIG-bench Machine LearningDomain Adaptation	—Unverified
DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking	May 1, 2025	Domain Adaptation	—Unverified
DE-PADA: Personalized Augmentation and Domain Adaptation for ECG Biometrics Across Physiological States	Feb 7, 2025	Domain AdaptationUser Identification	—Unverified
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation	Dec 15, 2023	Domain AdaptationSemantic Segmentation	—Unverified
Dense Retrieval Adaptation using Target Domain Description	Jul 6, 2023	Domain AdaptationInformation Retrieval	—Unverified
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data	Aug 1, 2021	Domain AdaptationFact Verification	—Unverified
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction	Jul 1, 2019	DiversityDomain Adaptation	—Unverified
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding	Dec 1, 2021	DisentanglementDomain Adaptation	—Unverified
A Multi-Domain Translation Model Framework for Statistical Machine Translation	Aug 1, 2013	Domain AdaptationMachine Translation	—Unverified
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction	Oct 16, 2023	Domain AdaptationEvent Extraction	—Unverified
Demographic Factors Improve Classification Performance	Jul 1, 2015	ClassificationDomain Adaptation	—Unverified
DeMIAN: Deep Modality Invariant Adversarial Network	Dec 23, 2016	Domain AdaptationGeneral Classification	—Unverified
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation	Mar 25, 2023	Domain AdaptationERP	—Unverified
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning	Dec 3, 2021	Domain AdaptationPseudo Label	—Unverified
De l'extraction des interactions m\'edicament-m\'edicament vers les interactions aliment-m\'edicament \`a partir de textes biom\'edicaux: Adaptation de domaine (From the extraction of drug-drug interactions to the food-drug interactions in biomedical texts : domain adaptation)	Jul 1, 2019	Domain AdaptationRelation	—Unverified

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All datasets Office-31 SYNTHIA-to-Cityscapes GTA5 to Cityscapes Office-Home VisDA2017 ImageCLEF-DA Cityscapes to ACDC MNIST-to-USPS SVHN-to-MNIST USPS-to-MNIST SVNH-to-MNIST MoLane

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	FFTAT	Average Accuracy	96	—	Unverified
2	PMTrans	Average Accuracy	95.3	—	Unverified
3	CMKD	Average Accuracy	94.4	—	Unverified
4	SSRT-B (ours)	Average Accuracy	93.5	—	Unverified
5	CDTrans	Average Accuracy	92.6	—	Unverified
6	CoVi	Average Accuracy	91.8	—	Unverified
7	GSDE	Average Accuracy	91.7	—	Unverified
8	FixBi	Average Accuracy	91.4	—	Unverified
9	Contrastive Adaptation Network	Average Accuracy	90.6	—	Unverified
10	BIWAA	Average Accuracy	90.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HALO	mIoU	78.1	—	Unverified
2	ILM-ASSL	mIoU	76.6	—	Unverified
3	DCF	mIoU	69.3	—	Unverified
4	HRDA+PiPa	mIoU	68.2	—	Unverified
5	MIC	mIoU	67.3	—	Unverified
6	FREDOM - Transformer	mIoU	67	—	Unverified
7	HRDA	mIoU	65.8	—	Unverified
8	SePiCo	mIoU	64.3	—	Unverified
9	MIC + Guidance Training	mIoU	63.8	—	Unverified
10	DAFormer + ProCST	mIoU	61.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HALO	mIoU	77.8	—	Unverified
2	DCF	mIoU	77.7	—	Unverified
3	ILM-ASSL	mIoU	76.1	—	Unverified
4	MIC	mIoU	75.9	—	Unverified
5	HRDA+PiPa	mIoU	75.6	—	Unverified
6	HRDA	mIoU	73.8	—	Unverified
7	FREDOM - Transformer	mIoU	73.6	—	Unverified
8	HALO	mIoU	73.3	—	Unverified
9	SePiCo	mIoU	70.3	—	Unverified
10	DAFormer + ProCST	mIoU	69.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SWG	Accuracy	92.3	—	Unverified
2	RCL	Accuracy	90	—	Unverified
3	PGA (ViT-L/14)	Accuracy	89.4	—	Unverified
4	PMTrans	Accuracy	89	—	Unverified
5	CMKD	Accuracy	89	—	Unverified
6	MIC	Accuracy	86.2	—	Unverified
7	PGA (ViT-B/16)	Accuracy	85.1	—	Unverified
8	ELS	Accuracy	84.6	—	Unverified
9	SDAT (ViT-B/16)	Accuracy	84.3	—	Unverified
10	CDTrans (DeiT-B)	Accuracy	80.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FFTAT	Accuracy	93.8	—	Unverified
2	RCL	Accuracy	93.2	—	Unverified
3	MIC	Accuracy	92.8	—	Unverified
4	SWG	Accuracy	92.7	—	Unverified
5	CMKD	Accuracy	91.8	—	Unverified
6	DePT	Accuracy	90.7	—	Unverified
7	SDAT(ViT)	Accuracy	89.8	—	Unverified
8	SFDA2++	Accuracy	89.6	—	Unverified
9	PMtrans	Accuracy	88.8	—	Unverified
10	CoVi	Accuracy	88.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CMKD	Accuracy	94.3	—	Unverified
2	MCC+NWD	Accuracy	90.7	—	Unverified
3	GLOT-DR	Accuracy	90.4	—	Unverified
4	SPL	Accuracy	90.3	—	Unverified
5	DFA-SAFN	Accuracy	90.2	—	Unverified
6	DADA	Accuracy	89.3	—	Unverified
7	DFA-ENT	Accuracy	89.1	—	Unverified
8	MEDM	Accuracy	88.9	—	Unverified
9	DDA	Accuracy	88.9	—	Unverified
10	IAFN+ENT	Accuracy	88.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SoRA	mIoU	78.8	—	Unverified
2	Rein	mIoU	77.6	—	Unverified
3	CoDA	mIoU	72.6	—	Unverified
4	Refign (HRDA)	mIoU	72.1	—	Unverified
5	HALO	mIoU	71.9	—	Unverified
6	MIC	mIoU	70.4	—	Unverified
7	HRDA	mIoU	68	—	Unverified
8	Refign (DAFormer)	mIoU	65.5	—	Unverified
9	VBLC (DAFormer)	mIoU	64.2	—	Unverified
10	CMFormer	mIoU	60.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FACT	Accuracy	98.8	—	Unverified
2	FAMCD	Accuracy	98.72	—	Unverified
3	DFA-MCD	Accuracy	98.6	—	Unverified
4	Mean teacher	Accuracy	98.26	—	Unverified
5	DRANet	Accuracy	98.2	—	Unverified
6	SHOT	Accuracy	98	—	Unverified
7	DFA-ENT	Accuracy	97.9	—	Unverified
8	CyCleGAN (Light-weight Calibrator)	Accuracy	97.1	—	Unverified