SOTAVerified|Agents Browse Leaderboard About

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 1111–1120 of 3687 papers

Title	Date	Tasks	Status	Hype
Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation	Feb 22, 2023	Multi-Task Learning	—Unverified	0
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network	Feb 22, 2023	ClassificationMulti-Task Learning	—Unverified	0
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation	Feb 22, 2023	Multi-Task LearningSpeech Enhancement	CodeCode Available	1
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning	Feb 22, 2023	Multi-Task Learning	CodeCode Available	1
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition	Feb 22, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks	Feb 22, 2023	Continual LearningMulti-Task Learning	—Unverified	0
Device Tuning for Multi-Task Large Model	Feb 21, 2023	modelMulti-Task Learning	—Unverified	0
MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis	Feb 21, 2023	Graph EmbeddingGraph Neural Network	—Unverified	0
Transductive Matrix Completion with Calibration for Multi-Task Learning	Feb 20, 2023	Matrix CompletionMulti-Label Learning	—Unverified	0
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness	Feb 18, 2023	Knowledge DistillationMulti-Task Learning	—Unverified	0

Show:10 25 50

← PrevPage 112 of 369Next →

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