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 5301–5325 of 6439 papers

Title	Date	Tasks	Status
REVISTING NEGATIVE TRANSFER USING ADVERSARIAL LEARNING	May 1, 2019	AttributeDomain Adaptation	—Unverified
Domain Adaptation for Structured Output via Disentangled Patch Representations	May 1, 2019	Domain AdaptationSemantic Segmentation	—Unverified
NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES	May 1, 2019	Domain AdaptationNeural Network Compression	—Unverified
Variational Domain Adaptation	May 1, 2019	Bayesian InferenceDomain Adaptation	—Unverified
On Scalable and Efficient Computation of Large Scale Optimal Transport	May 1, 2019	Domain Adaptation	—Unverified
Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift	May 1, 2019	Domain AdaptationFew-Shot Learning	—Unverified
Unsupervised Domain Adaptation for Distance Metric Learning	May 1, 2019	Domain AdaptationFace Recognition	—Unverified
Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification	Apr 30, 2019	Domain AdaptationImage-to-Image Translation	—Unverified
Deep multi-Wasserstein unsupervised domain adaptation	Apr 30, 2019	Domain AdaptationGeneralization Bounds	—Unverified
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration	Apr 30, 2019	Domain AdaptationTransfer Learning	—Unverified
Conditional Teacher-Student Learning	Apr 28, 2019	Domain AdaptationModel Compression	—Unverified
Deep Learning to Predict Student Outcomes	Apr 27, 2019	Deep LearningDomain Adaptation	—Unverified
Transferrable Prototypical Networks for Unsupervised Domain Adaptation	Apr 25, 2019	Domain AdaptationPseudo Label	—Unverified
Exploring Object Relation in Mean Teacher for Cross-Domain Detection	Apr 25, 2019	Domain AdaptationRelation	CodeCode Available
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification	Apr 24, 2019	Acoustic Scene ClassificationClassification	CodeCode Available
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis	Apr 24, 2019	Domain AdaptationGaze Estimation	—Unverified
Switchable Whitening for Deep Representation Learning	Apr 22, 2019	Domain Adaptationimage-classification	CodeCode Available
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages	Apr 21, 2019	Domain AdaptationSentiment Analysis	—Unverified
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation	Apr 21, 2019	Domain Adaptation	—Unverified
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing	Apr 20, 2019	Domain AdaptationUnsupervised KG-to-Text Generation	CodeCode Available
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes	Apr 19, 2019	Domain AdaptationSegmentation	—Unverified
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences	Apr 16, 2019	Domain AdaptationSpeaker Recognition	—Unverified
Active Adversarial Domain Adaptation	Apr 16, 2019	Active LearningDiversity	—Unverified
Automatic adaptation of object detectors to new domains using self-training	Apr 15, 2019	Domain AdaptationKnowledge Distillation	CodeCode Available
Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation	Apr 15, 2019	BIG-bench Machine LearningDomain 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