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 2976–3000 of 6439 papers

Title	Date	Tasks	Status
Adversarial Learning Networks: Source-free Unsupervised Domain Incremental Learning	Jan 28, 2023	Continual LearningDisease Prediction	—Unverified
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift	Aug 21, 2020	Domain AdaptationEvent Extraction	—Unverified
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity	Jun 21, 2020	Domain Adaptation	—Unverified
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation	Sep 1, 2024	DiagnosticDisease Prediction	—Unverified
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles	Oct 5, 2016	Domain AdaptationReinforcement Learning	—Unverified
Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition	Jun 27, 2023	Domain Adaptationspeech-recognition	—Unverified
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation	Sep 7, 2020	Autonomous DrivingDomain Adaptation	—Unverified
Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI	May 30, 2025	Domain AdaptationSegmentation	—Unverified
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease	Dec 1, 2016	Domain AdaptationTwo-sample testing	—Unverified
Instant Domain Augmentation for LiDAR Semantic Segmentation	Mar 25, 2023	Domain AdaptationLIDAR Semantic Segmentation	—Unverified
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation	Jan 3, 2023	Domain AdaptationSegmentation	—Unverified
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences	Apr 16, 2019	Domain AdaptationSpeaker Recognition	—Unverified
Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation	May 21, 2025	Data AugmentationDomain Adaptation	—Unverified
ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation	Dec 23, 2020	Domain AdaptationImage Segmentation	—Unverified
ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)	Dec 23, 2024	Domain Adaptation	—Unverified
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation	Oct 7, 2023	Contrastive LearningCross-corpus	—Unverified
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation	Sep 6, 2023	DecoderDomain Adaptation	—Unverified
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction	Jul 24, 2023	Action RecognitionDomain Adaptation	—Unverified
IDDA: a large-scale multi-domain dataset for autonomous driving	Apr 17, 2020	Autonomous DrivingDomain Adaptation	—Unverified
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing	Oct 28, 2021	Contrastive LearningDomain Adaptation	—Unverified
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report	Jul 7, 2022	Action RecognitionDomain Adaptation	—Unverified
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report	Jun 18, 2021	Action RecognitionDomain Adaptation	—Unverified
Identifying Key Concepts from EHR Notes Using Domain Adaptation	Sep 1, 2015	Domain AdaptationInformation Retrieval	—Unverified
A Survey on Deep Hashing Methods	Mar 4, 2020	Deep HashingDomain Adaptation	—Unverified
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints	Nov 23, 2015	Domain Adaptation	—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