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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 941–950 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains	May 31, 2019	Multi-Task Learning	CodeCode Available	0	5
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression	Apr 1, 2022	Multi-Task Learningregression	CodeCode Available	0	5
Human Keypoint Detection by Progressive Context Refinement	Oct 27, 2019	Human DetectionKeypoint Detection	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
Imbalanced multi-label classification using multi-task learning with extractive summarization	Mar 16, 2019	ClassificationExtractive Summarization	CodeCode Available	0	5
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes	Apr 4, 2024	In-Context LearningMulti-Task Learning	CodeCode Available	0	5
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection	Jun 15, 2024	Change DetectionEarth Observation	CodeCode Available	0	5
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation	May 1, 2022	Machine TranslationMulti-Task Learning	CodeCode Available	0	5
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization	May 13, 2024	Multi-Task Learning	CodeCode Available	0	5
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation	Aug 14, 2019	Image SegmentationMedical Image Segmentation	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