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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 3091–3100 of 3687 papers

Title	Date	Tasks	Status	Hype
Multi-task Learning for Influence Estimation and Maximization	Apr 18, 2019	Multi-Task LearningRepresentation Learning	CodeCode Available	0
Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds	Jul 24, 2021	Data AugmentationInstrument Recognition	CodeCode Available	0
Learning Gating ConvNet for Two-Stream based Methods in Action Recognition	Sep 12, 2017	Action ClassificationAction Recognition	CodeCode Available	0
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network	Jun 19, 2018	AnatomyMulti-Task Learning	CodeCode Available	0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	Mar 30, 2018	Multi-Task LearningNatural Language Inference	CodeCode Available	0
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling	Sep 23, 2021	Dialogue UnderstandingMulti-Task Learning	CodeCode Available	0
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization	Dec 31, 2021	Gaussian ProcessesL2 Regularization	CodeCode Available	0
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval	Nov 1, 2020	ArticlesMulti-Task Learning	CodeCode Available	0
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition	Oct 1, 2017	ClusteringFine-Grained Image Classification	CodeCode Available	0
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome	Aug 7, 2018	Missing ValuesMulti-Task Learning	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