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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 3391–3400 of 3687 papers

Title	Date	Tasks	Status	Hype
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval	Sep 1, 2018	Image RetrievalMulti-Task Learning	CodeCode Available	0
Generalizing Natural Language Analysis through Span-relation Representations	Nov 10, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification	Jan 31, 2024	Human ParsingMulti-Task Learning	CodeCode Available	0
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning	May 28, 2025	Multi-Task Learningparameter-efficient fine-tuning	CodeCode Available	0
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents	Nov 1, 2019	Learning-To-RankMulti-Hop Reading Comprehension	CodeCode Available	0
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection	Mar 9, 2021	Multi-Task LearningTransfer Learning	CodeCode Available	0
On the Automatic Generation of Medical Imaging Reports	Nov 22, 2017	Medical Report GenerationMulti-Task Learning	CodeCode Available	0
Morphological Segmentation for Seneca	Jun 1, 2021	DecoderModel Selection	CodeCode Available	0
Traffic flow prediction using Deep Sedenion Networks	Dec 7, 2020	Multi-Task LearningPrediction	CodeCode Available	0
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks	Mar 13, 2017	DecoderDependency Parsing	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