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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 931–940 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency	Apr 15, 2024	DecoderLanguage Modelling	CodeCode Available	0	5
Clustered Federated Learning via Generalized Total Variation Minimization	May 26, 2021	Distributed ComputingEdge-computing	CodeCode Available	0	5
Human Keypoint Detection by Progressive Context Refinement	Oct 27, 2019	Human DetectionKeypoint Detection	CodeCode Available	0	5
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning	Sep 9, 2023	Multi-Task LearningRecommendation Systems	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
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis	Sep 17, 2021	ChatbotMulti-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
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains	May 31, 2019	Multi-Task Learning	CodeCode Available	0	5
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge Distillation	Dec 24, 2024	Computational EfficiencyHandwritten Text Recognition	CodeCode Available	0	5
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection	Jun 15, 2024	Change DetectionEarth Observation	CodeCode Available	0	5

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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