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 2601–2625 of 6439 papers

Title	Date	Tasks	Status
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding	Aug 6, 2021	Domain AdaptationFew-Shot Learning	—Unverified
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection	Mar 26, 2021	3D Object DetectionAutonomous Driving	—Unverified
Filter-based Discriminative Autoencoders for Children Speech Recognition	Apr 1, 2022	DecoderDiversity	—Unverified
Filtered Manifold Alignment	Nov 11, 2020	Domain AdaptationTransfer Learning	—Unverified
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation	Jun 24, 2024	Domain AdaptationRepresentation Learning	—Unverified
Financial Aspect-Based Sentiment Analysis using Deep Representations	Aug 23, 2018	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering	Oct 26, 2018	Domain AdaptationQuestion Answering	—Unverified
Findings of the 2014 Workshop on Statistical Machine Translation	Jun 1, 2014	Cross-Lingual Information RetrievalDomain Adaptation	—Unverified
Findings of the LoResMT 2020 Shared Task on Zero-Shot for Low-Resource languages	Dec 1, 2020	Domain AdaptationMachine Translation	—Unverified
Findings of the Second Workshop on Neural Machine Translation and Generation	Jun 8, 2018	Data AugmentationDomain Adaptation	—Unverified
Adapting Models to Signal Degradation using Distillation	Apr 1, 2016	Domain AdaptationKnowledge Distillation	—Unverified
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation	Oct 16, 2021	8kDomain Adaptation	—Unverified
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation	Jun 2, 2022	8kDomain Adaptation	—Unverified
Geometric Unsupervised Domain Adaptation for Semantic Segmentation	Mar 30, 2021	Depth EstimationDepth Prediction	—Unverified
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings	Apr 6, 2020	Bilingual Lexicon InductionDomain Adaptation	—Unverified
Fine-grained Interpretation and Causation Analysis in Deep NLP Models	May 17, 2021	Domain Adaptation	—Unverified
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation	Sep 10, 2019	DiversityDomain Adaptation	—Unverified
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach	Sep 7, 2017	AttributeDomain Adaptation	—Unverified
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching	Mar 26, 2021	Domain AdaptationImage-to-Image Translation	—Unverified
Fine-grained Unsupervised Domain Adaptation for Gait Recognition	Jan 1, 2023	Domain AdaptationGait Recognition	—Unverified
Exploiting Neural Query Translation into Cross Lingual Information Retrieval	Oct 26, 2020	Cross-Lingual Information RetrievalData Augmentation	—Unverified
Finetuning Foundation Models for Joint Analysis Optimization	Jan 24, 2024	Domain Adaptation	—Unverified
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation	Jun 23, 2021	Domain AdaptationPseudo Label	—Unverified
Fine-tuning Large Language Models for Domain-specific Machine Translation	Feb 23, 2024	Domain AdaptationIn-Context Learning	—Unverified
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation	Nov 12, 2018	Domain AdaptationUnsupervised 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	CMKD	Accuracy	89	—	Unverified
5	PMTrans	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	DDA	Accuracy	88.9	—	Unverified
9	MEDM	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