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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 3141–3150 of 3687 papers

Title	Date	Tasks	Status	Hype
Towards Explainable Multimodal Depression Recognition for Clinical Interviews	Jan 27, 2025	Decision MakingDepression Detection	CodeCode Available	0
Improving Neural Story Generation by Targeted Common Sense Grounding	Aug 26, 2019	Common Sense ReasoningMulti-Task Learning	CodeCode Available	0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data	Mar 1, 2019	DenoisingGrammatical Error Correction	CodeCode Available	0
Learning to Represent Bilingual Dictionaries	Aug 10, 2018	Multi-Task LearningParaphrase Identification	CodeCode Available	0
Critical Learning Periods Emerge Even in Deep Linear Networks	Aug 23, 2023	Multi-Task Learning	CodeCode Available	0
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation	Nov 2, 2023	Multi-Task LearningSession-Based Recommendations	CodeCode Available	0
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks	Nov 7, 2018	Image SegmentationMulti-Task Learning	CodeCode Available	0
Asynchronous Multi-Task Learning	Sep 30, 2016	Multi-Task LearningTransfer Learning	CodeCode Available	0
Learning to Transfer for Traffic Forecasting via Multi-task Learning	Nov 27, 2021	BenchmarkingDomain Adaptation	CodeCode Available	0
Residual Multi-Task Learner for Applied Ranking	Oct 30, 2024	Multi-Task Learning	CodeCode Available	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