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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 1381–1390 of 3687 papers

Title	Date	Tasks	Status	Hype
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning	Jul 20, 2022	Feature EngineeringMulti-Task Learning	CodeCode Available	1
Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks	Jul 20, 2022	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified	0
Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge	Jul 20, 2022	Multi-Task Learning	—Unverified	0
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges	Jul 20, 2022	Multi-Task Learning	CodeCode Available	0
Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge	Jul 20, 2022	Multi-Task Learning	—Unverified	0
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation	Jul 19, 2022	Causal InferenceMulti-Task Learning	CodeCode Available	1
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images	Jul 19, 2022	Emotion RecognitionMulti-Task Learning	—Unverified	0
Multi-Task Learning Framework for Emotion Recognition in-the-wild	Jul 19, 2022	Emotion RecognitionMulti-Task Learning	CodeCode Available	0
SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition	Jul 19, 2022	Multi-Task Learning	CodeCode Available	0
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification	Jul 18, 2022	Cover song identificationData Augmentation	—Unverified	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