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 5851–5875 of 6439 papers

Title	Date	Tasks	Status
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions	Jul 20, 2017	Causal InferenceDomain Adaptation	CodeCode Available
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation	Jul 19, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
When Unsupervised Domain Adaptation Meets Tensor Representations	Jul 19, 2017	Domain AdaptationUnsupervised Domain Adaptation	CodeCode Available
Domain Adaptation for Resume Classification Using Convolutional Neural Networks	Jul 18, 2017	ClassificationDomain Adaptation	—Unverified
PAC-Bayes and Domain Adaptation	Jul 17, 2017	Domain AdaptationGeneralization Bounds	—Unverified
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL	Jul 13, 2017	Active LearningDomain Adaptation	—Unverified
Stable Distribution Alignment Using the Dual of the Adversarial Distance	Jul 13, 2017	Domain Adaptation	—Unverified
Deep Domain Adaptation by Geodesic Distance Minimization	Jul 13, 2017	Domain Adaptation	—Unverified
Zero-Shot Deep Domain Adaptation	Jul 6, 2017	ClassificationDomain Adaptation	—Unverified
Wasserstein Distance Guided Representation Learning for Domain Adaptation	Jul 5, 2017	Domain AdaptationGeneral Classification	CodeCode Available
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics	Jul 4, 2017	ClassificationDomain Adaptation	—Unverified
Domain Attention with an Ensemble of Experts	Jul 1, 2017	Domain AdaptationSpoken Language Understanding	—Unverified
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation	Jul 1, 2017	Domain AdaptationMachine Translation	—Unverified
Active Sentiment Domain Adaptation	Jul 1, 2017	Active LearningDomain Adaptation	—Unverified
Sentence Embedding for Neural Machine Translation Domain Adaptation	Jul 1, 2017	Domain AdaptationLanguage Modeling	—Unverified
An Unsupervised Neural Attention Model for Aspect Extraction	Jul 1, 2017	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Parser Adaptation for Social Media by Integrating Normalization	Jul 1, 2017	Domain AdaptationNamed Entity Recognition (NER)	—Unverified
Detecting annotation noise in automatically labelled data	Jul 1, 2017	Active LearningDomain Adaptation	—Unverified
Adversarial Adaptation of Synthetic or Stale Data	Jul 1, 2017	Domain AdaptationSpoken Language Understanding	—Unverified
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings	Jul 1, 2017	Domain AdaptationInformation Retrieval	—Unverified
Zero-Shot Classification With Discriminative Semantic Representation Learning	Jul 1, 2017	AttributeClassification	—Unverified
Distributional Adversarial Networks	Jun 29, 2017	Domain Adaptation	CodeCode Available
Target contrastive pessimistic risk for robust domain adaptation	Jun 25, 2017	Domain AdaptationSelection bias	CodeCode Available
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation	Jun 23, 2017	Domain AdaptationGeneral Classification	—Unverified
Causal Embeddings for Recommendation	Jun 23, 2017	Domain Adaptation	CodeCode Available

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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