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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 1171–1180 of 3687 papers

Title	Date	Tasks	Status	Hype
Hierarchical Prompt Learning for Multi-Task Learning	Jan 1, 2023	Multi-Task LearningPrompt Learning	—Unverified	0
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners	Jan 1, 2023	Mixture-of-ExpertsMulti-Task Learning	—Unverified	0
Multi-Label Compound Expression Recognition: C-EXPR Database & Network	Jan 1, 2023	Emotion RecognitionFacial Expression Recognition (FER)	—Unverified	0
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once	Jan 1, 2023	Incremental LearningMulti-Task Learning	—Unverified	0
Masked Representation Learning for Domain Generalized Stereo Matching	Jan 1, 2023	DecoderImage Reconstruction	—Unverified	0
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual Recognition	Jan 1, 2023	Classificationimage-classification	CodeCode Available	0
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing	Jan 1, 2023	Edge-computingFederated Learning	CodeCode Available	0
TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)	Dec 30, 2022	Domain AdaptationInformation Retrieval	—Unverified	0
A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific	Dec 29, 2022	Multi-Task Learning	—Unverified	0
Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds	Dec 23, 2022	Multi-Task LearningObject	CodeCode Available	1

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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