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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 1011–1020 of 3687 papers

Title	Date	Tasks	Status	Hype
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning	May 19, 2023	Multi-Task Learningspeech-recognition	—Unverified	0
A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds	May 18, 2023	Event DetectionMulti-Task Learning	—Unverified	0
Instruction Tuned Models are Quick Learners	May 17, 2023	In-Context LearningMulti-Task Learning	CodeCode Available	0
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images	May 17, 2023	Multi-Task LearningPrediction	CodeCode Available	1
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization	May 17, 2023	ClassificationContrastive Learning	—Unverified	0
Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences	May 16, 2023	Inductive BiasMachine Translation	CodeCode Available	0
When is an SHM problem a Multi-Task-Learning problem?	May 16, 2023	Multi-Task LearningStructural Health Monitoring	—Unverified	0
Multi-task convolutional neural network for image aesthetic assessment	May 16, 2023	Multi-Task Learning	—Unverified	0
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning	May 15, 2023	Multimodal Sentiment AnalysisMulti-Task Learning	—Unverified	0
Understanding and Bridging the Modality Gap for Speech Translation	May 15, 2023	Machine TranslationMulti-Task Learning	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