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 191–200 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning	Aug 18, 2021	Multi-Task Learning	CodeCode Available	1	5
APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning	Nov 1, 2020	Argument Pair Extraction (APE)Multi-Task Learning	CodeCode Available	1	5
Deeper Task-Specificity Improves Joint Entity and Relation Extraction	Feb 15, 2020	Joint Entity and Relation ExtractionMulti-Task Learning	CodeCode Available	1	5
Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System	Feb 10, 2023	Multi-Task LearningRecommendation Systems	CodeCode Available	1	5
A Multi-Task Deep Learning Framework for Building Footprint Segmentation	Apr 19, 2021	Boundary DetectionDeep Learning	CodeCode Available	1	5
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis	Apr 4, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1	5
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds	Mar 11, 2020	Instance SegmentationMulti-Task Learning	CodeCode Available	1	5
Federated Multi-Task Learning under a Mixture of Distributions	Aug 23, 2021	FairnessFederated Learning	CodeCode Available	1	5
Deep Learning for Automatic Pneumonia Detection	May 28, 2020	Deep LearningDiagnostic	CodeCode Available	1	5
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation	Jun 5, 2023	Instance SegmentationMulti-Task Learning	CodeCode Available	1	5

Show:10 25 50

← PrevPage 20 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