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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 981–990 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery	Mar 10, 2020	Multi-Task LearningScene Understanding	CodeCode Available	0	5
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning	Nov 2, 2023	Contrastive LearningMulti-Task Learning	CodeCode Available	0	5
HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots	Jan 29, 2023	Multi-Task LearningQuestion Answering	CodeCode Available	0	5
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization	Oct 19, 2020	Multi-Task LearningRetrieval	CodeCode Available	0	5
Boundary-aware Context Neural Network for Medical Image Segmentation	May 3, 2020	Image SegmentationMedical Image Segmentation	CodeCode Available	0	5
Privacy-preserving Task-Agnostic Vision Transformer for Image Processing	Sep 29, 2021	Multi-Task LearningPrivacy Preserving	CodeCode Available	0	5
Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters	Jan 1, 2024	Multi-Task LearningOptical Character Recognition	CodeCode Available	0	5
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity	Nov 1, 2021	Auxiliary LearningMRPC	CodeCode Available	0	5
Efficient Multitask Dense Predictor via Binarization	May 23, 2024	BinarizationKnowledge Distillation	CodeCode Available	0	5
A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs	Sep 17, 2024	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