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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 581–590 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction	Nov 7, 2023	AttributeAttribute Value Extraction	CodeCode Available	0	5
Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression Recognition	Apr 2, 2024	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	0	5
Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning	Nov 14, 2019	Generalization BoundsMulti-Label Learning	CodeCode Available	0	5
KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning	Sep 19, 2023	Face VerificationFairness	CodeCode Available	0	5
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources	Aug 24, 2020	Machine Reading ComprehensionMulti-Task Learning	CodeCode Available	0	5
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat	Sep 10, 2018	Multi-Task LearningReinforcement Learning	CodeCode Available	0	5
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology	Mar 6, 2024	Decision MakingMulti-Task Learning	CodeCode Available	0	5
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network	Jul 10, 2019	DeblurringImage Restoration	CodeCode Available	0	5
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation	Sep 9, 2019	Multi-Task LearningSaliency Detection	CodeCode Available	0	5
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning	Sep 21, 2016	DecoderMulti-Task Learning	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