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 5951–5975 of 6439 papers

Title	Date	Tasks	Status
Frustratingly Easy Neural Domain Adaptation	Dec 1, 2016	Domain Adaptation	—Unverified
Fast Gated Neural Domain Adaptation: Language Model as a Case Study	Dec 1, 2016	Domain AdaptationLanguage Modeling	—Unverified
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks	Dec 1, 2016	Active LearningBIG-bench Machine Learning	—Unverified
A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation	Dec 1, 2016	Domain AdaptationLanguage Modeling	—Unverified
A Deep Fusion Model for Domain Adaptation in Phrase-based MT	Dec 1, 2016	Domain AdaptationMachine Translation	—Unverified
Leveraging Multiple Domains for Sentiment Classification	Dec 1, 2016	ClassificationDomain Adaptation	—Unverified
Joint Learning of Local and Global Features for Entity Linking via Neural Networks	Dec 1, 2016	Domain AdaptationEntity Linking	—Unverified
NICT-2 Translation System for WAT2016: Applying Domain Adaptation to Phrase-based Statistical Machine Translation	Dec 1, 2016	Domain AdaptationMachine Translation	—Unverified
POS Tagging Experts via Topic Modeling	Dec 1, 2016	Domain AdaptationPart-Of-Speech Tagging	—Unverified
Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation	Dec 1, 2016	Domain AdaptationMachine Translation	—Unverified
Tuning Bayes Baseline for Dialect Detection	Dec 1, 2016	Domain Adaptation	—Unverified
Detecting Uncertainty Cues in Hungarian Social Media Texts	Dec 1, 2016	BIG-bench Machine LearningDomain Adaptation	—Unverified
Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners' Writings	Dec 1, 2016	ClassificationDomain Adaptation	—Unverified
Challenges and Solutions for Latin Named Entity Recognition	Dec 1, 2016	Active LearningDomain Adaptation	—Unverified
Modeling non-standard language	Dec 1, 2016	Domain Adaptation	—Unverified
Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings	Dec 1, 2016	Domain Adaptationnamed-entity-recognition	—Unverified
Towards Accurate Word Segmentation for Chinese Patents	Nov 30, 2016	Chinese Word SegmentationDomain Adaptation	—Unverified
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives	Nov 28, 2016	Domain AdaptationMulti-Task Learning	—Unverified
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images	Nov 28, 2016	ClusteringDomain Adaptation	—Unverified
Invariant Representations for Noisy Speech Recognition	Nov 27, 2016	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Learning an Invariant Hilbert Space for Domain Adaptation	Nov 25, 2016	Domain AdaptationRiemannian optimization	—Unverified
Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors	Nov 24, 2016	Domain Adaptation	—Unverified
Exploiting Web Images for Dataset Construction: A Domain Robust Approach	Nov 22, 2016	Domain Adaptationimage-classification	—Unverified
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest	Nov 9, 2016	Domain AdaptationGeneral Classification	—Unverified
Domain Adaptation with L2 constraints for classifying images from different endoscope systems	Nov 8, 2016	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