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 2301–2325 of 6439 papers

Title	Date	Tasks	Status
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found	Dec 9, 2023	Domain AdaptationLanguage Modeling	—Unverified
Implicit Steganography Beyond the Constraints of Modality	Dec 9, 2023	Domain Adaptation	—Unverified
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen	Dec 9, 2023	Domain AdaptationSegmentation	—Unverified
Multi-source domain adaptation for regression	Dec 9, 2023	Domain AdaptationEnsemble Learning	—Unverified
Subject-Based Domain Adaptation for Facial Expression Recognition	Dec 9, 2023	Domain AdaptationFacial Expression Recognition	CodeCode Available
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation	Dec 8, 2023	Active LearningDomain Adaptation	—Unverified
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting	Dec 8, 2023	Domain AdaptationPartial Domain Adaptation	—Unverified
Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models	Dec 7, 2023	Domain AdaptationMedical Report Generation	CodeCode Available
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance	Dec 7, 2023	Domain AdaptationKnowledge Distillation	CodeCode Available
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition	Dec 6, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Corporate Bankruptcy Prediction with Domain-Adapted BERT	Dec 6, 2023	Domain AdaptationLanguage Modeling	—Unverified
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift	Dec 6, 2023	Contrastive LearningDomain Adaptation	—Unverified
Unknown Sample Discovery for Source Free Open Set Domain Adaptation	Dec 5, 2023	Domain Adaptation	—Unverified
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic	Dec 5, 2023	Data AugmentationDomain Adaptation	—Unverified
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs	Dec 5, 2023	Action SegmentationAll	CodeCode Available
Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training	Dec 5, 2023	Action RecognitionDomain Adaptation	CodeCode Available
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images	Dec 4, 2023	DiversityDomain Adaptation	—Unverified
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations	Dec 4, 2023	Anomaly DetectionDomain Adaptation	—Unverified
Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CPM^2C)	Dec 2, 2023	Action RecognitionDomain Adaptation	CodeCode Available
Target-agnostic Source-free Domain Adaptation for Regression Tasks	Dec 1, 2023	Domain AdaptationPrediction	—Unverified
Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry	Dec 1, 2023	Domain Adaptation	—Unverified
Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation	Nov 30, 2023	Domain Adaptation	—Unverified
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation	Nov 30, 2023	Contrastive LearningDomain Adaptation	—Unverified
Learning Part Segmentation from Synthetic Animals	Nov 30, 2023	Domain AdaptationPseudo Label	—Unverified
Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition	Nov 30, 2023	class-incremental learningClass Incremental Learning	CodeCode Available

Show:10 25 50

← PrevPage 93 of 258Next →

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