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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 691–700 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity	Sep 5, 2019	Language ModelingLanguage Modelling	CodeCode Available	0	5
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems	Apr 14, 2022	Multi-Task LearningRecommendation Systems	CodeCode Available	0	5
Multi-task Learning for Influence Estimation and Maximization	Apr 18, 2019	Multi-Task LearningRepresentation Learning	CodeCode Available	0	5
Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization	Sep 20, 2023	coreference-resolutionCoreference Resolution	CodeCode Available	0	5
A Unified Mammogram Analysis Method via Hybrid Deep Supervision	Aug 31, 2018	ClassificationGeneral Classification	CodeCode Available	0	5
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning	Jan 28, 2020	DecoderGoal-Oriented Dialog	CodeCode Available	0	5
A Unified Framework for Multi-intent Spoken Language Understanding with prompting	Oct 7, 2022	Intent DetectionMulti-Task Learning	CodeCode Available	0	5
A Unified Framework for Generalizable Style Transfer: Style and Content Separation	Jun 13, 2018	DecoderMulti-Task Learning	CodeCode Available	0	5
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning	Aug 1, 2021	DenoisingFew-Shot Learning	CodeCode Available	0	5
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation	Mar 20, 2025	Multi-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