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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 901–910 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model	Dec 21, 2024	Medical Image AnalysisMulti-Task Learning	CodeCode Available	0	5
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization	Mar 2, 2020	image-classificationImage Classification	CodeCode Available	0	5
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data	Mar 14, 2018	Multi-Task LearningTransfer Learning	CodeCode Available	0	5
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives	Dec 16, 2022	Multi-Task LearningVariable Selection	CodeCode Available	0	5
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans	Mar 9, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	0	5
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension	Nov 1, 2019	Machine Reading ComprehensionMulti-Task Learning	CodeCode Available	0	5
Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events	Jun 18, 2021	Deep Reinforcement LearningMulti-Task Learning	CodeCode Available	0	5
Hybrid Losses for Hierarchical Embedding Learning	Jan 22, 2025	Multi-Task LearningRetrieval	CodeCode Available	0	5
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings	Aug 1, 2018	General ClassificationMulti-Task Learning	CodeCode Available	0	5
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning	Oct 16, 2020	DiversityMulti-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