SOTAVerified|Agents Browse Leaderboard About

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 1051–1060 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit	Aug 22, 2022	Dependency ParsingMorphological Tagging	CodeCode Available	0	5
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks	Nov 14, 2018	Inductive BiasMulti-Task Learning	CodeCode Available	0	5
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization	May 13, 2024	Multi-Task Learning	CodeCode Available	0	5
Hybrid Losses for Hierarchical Embedding Learning	Jan 22, 2025	Multi-Task LearningRetrieval	CodeCode Available	0	5
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation	May 1, 2022	Machine TranslationMulti-Task Learning	CodeCode Available	0	5
Comparison of single and multitask learning for predicting cognitive decline based on MRI data	Sep 21, 2021	DiagnosticDomain Adaptation	CodeCode Available	0	5
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling	Apr 30, 2021	ClassificationGeneral Classification	CodeCode Available	0	5
HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots	Jan 29, 2023	Multi-Task LearningQuestion Answering	CodeCode Available	0	5
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition	Mar 3, 2016	Face DetectionMulti-Task Learning	CodeCode Available	0	5
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction	Feb 4, 2023	Few-Shot LearningMolecular Docking	CodeCode Available	0	5

Show:10 25 50

← PrevPage 106 of 369Next →

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