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

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Showing 4051–4075 of 6439 papers

Title	Date	Tasks	Status
5W1H Extraction With Large Language Models	May 25, 2024	Domain AdaptationEvent Extraction	—Unverified
70B-parameter large language models in Japanese medical question-answering	Jun 21, 2024	Continual PretrainingDomain Adaptation	—Unverified
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation	Sep 12, 2023	Domain AdaptationImage Segmentation	—Unverified
Abductive Reasoning as Self-Supervision for Common Sense Question Answering	Sep 6, 2019	Common Sense ReasoningDomain Adaptation	—Unverified
A Brief Review of Domain Adaptation	Oct 7, 2020	BIG-bench Machine LearningDomain Adaptation	—Unverified
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning	Mar 25, 2019	Domain AdaptationTransfer Learning	—Unverified
Accurate Unsupervised Joint Named-Entity Extraction from Unaligned Parallel Text	Jul 1, 2012	Cross-Domain Named Entity RecognitionDomain Adaptation	—Unverified
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation	Oct 27, 2023	DenoisingDomain Adaptation	—Unverified
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions	Jul 1, 2022	Domain AdaptationFault Diagnosis	—Unverified
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation	Jan 29, 2024	Domain AdaptationUnsupervised Domain Adaptation	—Unverified
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup	Oct 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
A Common Case of Jekyll and Hyde: The Synergistic Effect of Using Divided Source Training Data for Feature Augmentation	Oct 1, 2013	Chinese Word SegmentationDomain Adaptation	—Unverified
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation	Mar 12, 2017	ClassificationDomain Adaptation	—Unverified
A comparative analysis of embedding models for patent similarity	Mar 25, 2024	Domain AdaptationSentence	—Unverified
Supervised domain adaptation for building extraction from off-nadir aerial images	Nov 7, 2023	Domain AdaptationEarth Observation	—Unverified
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping	Jun 6, 2018	Domain AdaptationUnsupervised Domain Adaptation	—Unverified
A Comparison of Sentence-Weighting Techniques for NMT	Aug 1, 2021	ClassificationDomain Adaptation	—Unverified
A Comparison of Strategies for Source-Free Domain Adaptation	Aug 17, 2021	Active LearningData Augmentation	—Unverified
Towards Understanding Domain Adapted Sentence Embeddings for Document Retrieval	Jun 18, 2024	Domain AdaptationQuestion Answering	—Unverified
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques	Jan 24, 2025	Data AugmentationDomain Adaptation	—Unverified
A Comprehensive Survey on Source-free Domain Adaptation	Feb 23, 2023	Domain AdaptationSource-Free Domain Adaptation	—Unverified
A contribution to Optimal Transport on incomparable spaces	Nov 9, 2020	BIG-bench Machine LearningDomain Adaptation	—Unverified
A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping	Apr 9, 2025	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
A cross-domain recommender system using deep coupled autoencoders	Dec 8, 2021	Domain AdaptationRecommendation Systems	—Unverified
A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine	Oct 9, 2020	Domain AdaptationRepresentation Learning	—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