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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2151–2160 of 3687 papers

Title	Date	Tasks	Status	Hype
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection	Mar 9, 2021	Multi-Task LearningTransfer Learning	CodeCode Available	0
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles	Mar 8, 2021	ArticlesDiagnostic	CodeCode Available	1
"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays	Mar 8, 2021	Argument MiningClassification	—Unverified	0
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT	Mar 5, 2021	AttributeComputed Tomography (CT)	—Unverified	0
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification	Mar 5, 2021	Face VerificationMulti-Task Learning	—Unverified	0
MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis	Mar 5, 2021	BIG-bench Machine LearningMulti-Task Learning	—Unverified	0
Multi-task Learning by Leveraging the Semantic Information	Mar 3, 2021	Multi-Task Learning	—Unverified	0
RotoGrad: Gradient Homogenization in Multitask Learning	Mar 3, 2021	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	CodeCode Available	1
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data	Mar 2, 2021	Active LearningMulti-Task Learning	—Unverified	0
Incorporating VAD into ASR System by Multi-task Learning	Mar 2, 2021	Action DetectionActivity Detection	—Unverified	0

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All datasets QM9 Cityscapes test NYUv2 OMNIGLOT CelebA UTKFace wireframe dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PCGrad	∆m%	125.7	—	Unverified
2	CAGrad	∆m%	112.8	—	Unverified
3	IMTL-G	∆m%	77.2	—	Unverified
4	Nash-MTL	∆m%	62	—	Unverified
5	BayesAgg-MTL	∆m%	53.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	mIoU	76.41	—	Unverified
2	Nash-MTL	mIoU	75.41	—	Unverified
3	MultiObjectiveOptimization	mIoU	66.63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	Mean IoU	58.14	—	Unverified
2	Nash-MTL	Mean IoU	40.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Gumbel-Matrix Routing	Average Accuracy	93.52	—	Unverified
2	Mixture-of-Experts	Average Accuracy	92.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MGDA-UB	Error	8.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BayesAgg-MTL	delta_m	-2.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LETR	FH	83.3	—	Unverified