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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 541–550 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
An End-to-End Multi-Task Learning Model for Image-based Table Recognition	Mar 15, 2023	Cell DetectionDecoder	CodeCode Available	1	5
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis	Feb 9, 2021	Multimodal Sentiment AnalysisMulti-Task Learning	CodeCode Available	1	5
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization	Aug 1, 2021	Knowledge GraphsMedical Named Entity Recognition	CodeCode Available	1	5
Continued Pretraining for Better Zero- and Few-Shot Promptability	Oct 19, 2022	Language ModelingLanguage Modelling	CodeCode Available	1	5
AdaMerging: Adaptive Model Merging for Multi-Task Learning	Oct 4, 2023	modelMulti-Task Learning	CodeCode Available	1	5
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration	Jun 25, 2024	Change DetectionMulti-Task Learning	CodeCode Available	1	5
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality	Jul 1, 2020	Multimodal Sentiment AnalysisMulti-Task Learning	CodeCode Available	1	5
CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition	May 27, 2019	DecoderLanguage Modelling	CodeCode Available	1	5
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints	Dec 31, 2021	3D Lane DetectionAutonomous Vehicles	CodeCode Available	1	5
Continual Learning in Task-Oriented Dialogue Systems	Dec 31, 2020	Continual LearningIntent Recognition	CodeCode Available	1	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