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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 3181–3190 of 3687 papers

Title	Date	Tasks	Status	Hype
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse	Oct 3, 2023	feature selectionimage-classification	CodeCode Available	0
A Survey on Multi-Task Learning	Jul 25, 2017	Active LearningClustering	CodeCode Available	0
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning	Sep 9, 2023	Multi-Task LearningRecommendation Systems	CodeCode Available	0
Imbalanced multi-label classification using multi-task learning with extractive summarization	Mar 16, 2019	ClassificationExtractive Summarization	CodeCode Available	0
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation	May 21, 2025	Language ModelingLanguage Modelling	CodeCode Available	0
A Survey of Generative Information Retrieval	Jun 3, 2024	Information RetrievalMulti-Task Learning	CodeCode Available	0
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning	May 17, 2022	AllMulti-Task Learning	CodeCode Available	0
Lexicon information in neural sentiment analysis: a multi-task learning approach	Sep 1, 2019	Multi-Task LearningSentence	CodeCode Available	0
L-HYDRA: Multi-Head Physics-Informed Neural Networks	Jan 5, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	0
Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)	Sep 6, 2017	Multi-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