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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 791–800 of 3687 papers

Title	Date	Tasks	Status	Hype
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning	Oct 16, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	0
Learning visual-based deformable object rearrangement with local graph neural networks	Oct 16, 2023	Graph Neural NetworkMulti-Task Learning	CodeCode Available	0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems	Oct 16, 2023	Learning-To-RankMulti-Task Learning	—Unverified	0
New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach	Oct 15, 2023	Multi-Task Learning	—Unverified	0
Federated Multi-Objective Learning	Oct 15, 2023	Federated LearningMulti-Task Learning	—Unverified	0
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning	Oct 14, 2023	Image ClassificationImage Description	CodeCode Available	7
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning	Oct 13, 2023	Auxiliary LearningDisentanglement	—Unverified	0
Mitigating Bias for Question Answering Models by Tracking Bias Influence	Oct 13, 2023	Multiple-choiceMulti-Task Learning	—Unverified	0
Scalarization for Multi-Task and Multi-Domain Learning at Scale	Oct 13, 2023	Multi-Task LearningTransfer Learning	—Unverified	0
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images	Oct 13, 2023	Contrastive LearningDiagnostic	—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